text:
Scientific Publishing Europe Ltd.},
  doi = {10.1142/S1469026819500032},
  journal = {International Journal of Computational Intelligence and Applications},
  bibtexkey = {Wang2019},
  author = {Wang, C., Han, D., Fan, W., Liu, Q.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {Aspect extraction from bangla reviews using convolutional neural network},
  abstract = {The extensive customer reviews in web assist customers for purchase-decision-making as well as providers for business planning. Summarization of reviews is desirable as reading all reviews is not feasible to evaluate properly. To find aspect categories from reviews is a sub-task of summarization known as Aspect Based Sentiment Analysis (ABSA). In this paper, we present two Bangla datasets to perform ABSA task. We collected user comments on cricket game and annotated manually. The other dataset consists of consumer reviews of restaurants. A model to extract aspect category based on Convo-lutional Neural Network (CNN) is presented. The model shows the convincing performance of the proposed datasets compared to the conventional classifiers. © 2018 IEEE.},
  doi = {10.1109/ICIEV.2018.8641050},
  journal = {2018 Joint 7th International Conference on Informatics, Electronics and Vision and 2nd International Conference on Imaging, Vision and Pattern Recognition, ICIEV-IVPR 2018},
  bibtexkey = {Rahman2019262},
  author = {Rahman, M.A., Kumar Dey, E.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models},
  abstract = {Nowadays, video game developers record every virtual action performed by their players. As each player can remain in the game for years, this results in an exceptionally rich dataset that can be used to understand and predict player behavior. In particular, this information may serve to identify the most valuable players and foresee the amount of money they will spend in in-app purchases during their lifetime. This is crucial in free-to-play games, where up to 50% of the revenue is generated by just around 2% of the players, the so-called whales.To address this challenge, we explore how deep neural networks can be used to predict customer lifetime value in video games, and compare their performance to parametric models such as Pareto/NBD. Our results suggest that convolutional neural network structures are the most efficient in predicting the economic value of individual players. They not only perform better in terms of accuracy, but also scale to big data and significantly reduce computational time, as they can work directly with raw sequential data and thus do not require any feature engineering process. This becomes important when datasets are very large, as is often the case with video game logs.Moreover, convolutional neural networks are particularly well suited to identify potential whales. Such an early identification is of paramount importance for business purposes, as it would allow developers to implement in-game actions aimed at retaining big spenders and maximizing their lifetime, which would ultimately translate into increased revenue. © 2018 IEEE.},
  doi = {10.1109/BigData.2018.8622151},
  journal = {Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018},
  bibtexkey = {Chen20192134},
  author = {Chen, P.P., Guitart, A., Del RÃo, A.F., PeriÃ¡Ã±ez, A.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {An Adaptive Pre-clustering Support Vector Machine for Binary Imbalanced Classification},
  abstract = {Imbalance classification is a common but critical problem in machine learning and artificial intelligence. Derived from structural risk minimization, a Support Vector Machine (SVM) enjoys great reputation in classification. However, the original SVM is not suitable for the imbalance classification and the existing modifications of SVM for this kind of problems fail to take the distribution of datasets into full consideration, thereby leading to some remarkable loss in their classification performance. Recently, an Adaptive Clustering by Fast Search and Find of Density Peaks (ADPclust) is proposed and performs well in finding cluster centroids in a sample space automatically and more reliably by using adaptive density peak detection and silhouette theory. Motivated by this, this work proposes an adaptive pre-clustering SVM (AP-SVM) such that the information of the original dataset distribution is well utilized to yield balanced sub-datasets for accurate and efficient classification. Specifically, AP-SVM clusters the majority into several groups given a dataset and then applies undersampling on every cluster to re-balance the dataset to be used in the SVM classification step. After experiments on 10 binary public datasets and evaluation using Area Under Curve (AUC), F-Measure, G-Mean, we well show the superiority of the proposed method over SVM, Synthetic Minority Over-sampling Technique algorithm (SMOTE), Undersampling-SVM (U-SVM), K-Means, Fuzzy C Means and EasyEnsemble. © 2018 IEEE.},
  doi = {10.1109/SMC.2018.00124},
  journal = {Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018},
  bibtexkey = {Di2019681},
  author = {Di, Z., Yao, S., Kang, Q., Zhou, M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Sampling method based on improved C4.5 decision tree and its application in prediction of telecom customer churn},
  abstract = {Nowadays, customer churn prediction is quite important for telecom operators to reduce churn rate and remain competitive. However, the imbalance between the retained customers and the churners affects the prediction accuracy. For solving this problem, a new sampling method based on improved C4.5 decision tree is proposed. Firstly, an initial weight is set for each sample according to the data scale of each class. Then, the samples' weight is adjusted through several rounds of alternative training by the improved C4.5 decision tree algorithm. Both the gain ratio and the misclassification cost are considered for splitting criterion. Besides, the boundary minority examples and the centre majority examples are found according to their weights. Furthermore, over-sampling is conducted for the boundary minority examples by synthetic minority over-sampling technique (SMOTE) and under-sampling is executed for the majority examples. Experiments on UCI public data and telecom operator data show the efficiency of the new method. Copyright © 2019 Inderscience Enterprises Ltd.},
  doi = {10.1504/IJITM.2019.097887},
  journal = {International Journal of Information Technology and Management},
  bibtexkey = {Deng201993},
  author = {Deng, W., Deng, L., Liu, J., Qi, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {An efficient noise-filtered ensemble model for customer churn analysis in aviation industry},
  abstract = {Aviation customer churn analysis is a difficult point, which has puzzled over airlines. The difficulties lie in the imbalance of customer churn data distribution and noisy data interference. Although some existing sampling techniques and ensemble models are good at dealing with class imbalance problem, noisy examples in dataset seriously affects the sampling quality and predictive accuracy of classifiers. Therefore, the purpose of our work is to effectively solve the problem of noise interference in imbalanced data classification and improve the effect of the ensemble classifier. In this paper, we propose a novel noise filtering algorithm that combined Tomek-link with distance weighted KNN (TWK), which can effectively filter the noise from both minority and majority class in the imbalanced dataset and prevent relative value samples from being rejected by mistake. We integrate TWK and feature sampling into EasyEnsemble to get a new ensemble model, named FSEE-TWK for short, for customer churn analysis. The introduction of feature sampling to FSEE-TWK accelerate the process of training and avoid model over-fitting. We obtained imbalanced customer data from a major Chinese airline to predict potential churn customers. We use F-Measure and G-Mean to evaluate the performance of the new ensemble model. The experimental results show that the proposed model can effectively improve the classification of datasets and significantly reduce the training time of the model. © 2019 - IOS Press and the authors. All rights reserved.},
  doi = {10.3233/JIFS-182807},
  journal = {Journal of Intelligent and Fuzzy Systems},
  bibtexkey = {Li20192575},
  author = {Li, Y., Wei, J., Kang, K., Wu, Z.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {Multi-Class Learning from Label Proportions for Bank Customer Classification},
  abstract = {In this paper, we study multi-class learning from label proportions, and apply it to bank customer classification, in order to provide advice for banks to better manage customer relationships. We attempt to apply the multi-class extreme learning machine (ELM) to learning from label proportions (LLP). With a structure similar to neural network, ELM possesses higher computational speed and better generalization ability. As a result, it is suitable to deal with large-scale and multi-class problems. LLP is a learning problem, which classifies training data into bags where only the label proportions of each class in each bag is available. Furthermore, in order to maintain the stable model accuracy when the bag sizes increasing, we manage to add small number of labeled samples in our model, called LLP-ELM with in a semi-supervised learning framework. The experiments prove that our improvement has advantages in the case of large bag sizes. In practical, it is worth to consider to apply the proposed algorithm to multi-class learning from label proportions for bank customer classification and other multi-class scenarios. © 2020 The Authors. Published by Elsevier B.V.},
  doi = {10.1016/j.procs.2019.12.006},
  journal = {Procedia Computer Science},
  bibtexkey = {Qian2019421},
  author = {Qian, Y., Tong, Q., Wang, B.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {Making Machine Learning Forget},
  abstract = {Machine learning models often overfit to the training data and do not learn general patterns like humans do. This allows an attacker to learn private membership or attributes about the training data, simply by having access to the machine learning model. We argue that this vulnerability of current machine learning models makes them indirect stores of the personal data used for training and therefore, corresponding data protection regulations must apply to machine learning models as well. In this position paper, we specifically analyze how the “right-to-be-forgotten” provided by the European Union General Data Protection Regulation can be implemented on current machine learning models and which techniques can be used to build future models that can forget. This document also serves as a call-to-action for researchers and policy-makers to identify other technologies that can be used for this purpose. © Springer Nature Switzerland AG 2019.},
  doi = {10.1007/978-3-030-21752-5_6},
  journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  bibtexkey = {Shintre201972},
  author = {Shintre, S., Roundy, K.A., Dhaliwal, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {A Spammer Identification Method for Class Imbalanced Weibo Datasets},
  abstract = {Nowadays, Weibo has become a significant and popular information sharing platform in China. Meanwhile, spammer identification has been a big challenge for it. To mitigate the damage caused by spammers, classification algorithms from machine learning have been applied to distinguish spammers and non-spammers. However, most of the previous studies overlook the class imbalance problem of real-world data. In this paper, by analyzing the characteristics of spammers in Weibo, we select microblog content similarity, the average number of links, and the other 12 features to construct a comprehensive feature vector never seen before. Considering the existence of imbalance problems in spammer identification, an ensemble learning method is used to combine multiple base classifiers for improving the learning performance. During the training stage of base learners, fuzzy-logic-based oversampling and cost-sensitive support vector machine are considered to tackle imbalanced data at both data and algorithmic levels. The experimental results demonstrate that compared with the existing state-of-the-art methods, the recall rate of our proposed approach increases by 6.5% and reaches the precision value of 87.53% when used to deal with real-world Weibo datasets we collected. © 2019 IEEE.},
  doi = {10.1109/ACCESS.2019.2901756},
  journal = {IEEE Access},
  bibtexkey = {Tang201929193},
  author = {Tang, W., Ding, Z., Zhou, M.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {Variational bayes for mixture models with censored data},
  abstract = {In this paper, we propose a variational Bayesian algorithm for mixture models that can deal with censored data, which is the data under the situation that the exact value is known only when the value is within a certain range and otherwise only partial information is available. The proposed algorithm can be applied to any mixture model whose component distribution belongs to exponential family; it is a natural generalization of the variational Bayes that deals with “standard” samples whose values are known. We confirm the effectiveness of the proposed algorithm by experiments on synthetic and real world data. © 2019, Springer Nature Switzerland AG.},
  doi = {10.1007/978-3-030-10928-8_36},
  journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  bibtexkey = {Kohjima2019605},
  author = {Kohjima, M., Matsubayashi, T., Toda, H.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {LFit: An unsupervised discretization method based on the Ramerâ€“Douglasâ€“Peucker algorithm},
  abstract = {Discretization is the process of converting continuous values into discrete values. It is a preprocessing step of several machine learning and data mining algorithms and the quality of discretization may drastically affect the performance of these algorithms. In this study we propose a discretization algorithm, namely line fitting-based discretization (lFIT), based on the Ramer–Douglas–Peucker algorithm. It is a static, univariate, unsupervised, splitting-based, global, and incremental discretization method where intervals are determined based on the Ramer–Douglas–Peucker algorithm and the quality of partitioning is assessed based on the standard error of the estimate. To evaluate the performance of the proposed method, a set of experiments are conducted on ten benchmark datasets and the achieved results are compared to those obtained by eight state-of-the-art discretization methods. Experimental results show that lFIT achieves higher predictive accuracy and produces less number of inconsistency while it generates larger number of intervals. The obtained results are also validated through Friedman’s test and Holm’s post hoc test which revealed the fact that lFIT produces discretization schemes that statistically comply both with supervised and unsupervised discretization methods. © TÜBITAK},
  doi = {10.3906/elk-1806-192},
  journal = {Turkish Journal of Electrical Engineering and Computer Sciences},
  bibtexkey = {Mutlu20192344},
  author = {Mutlu, A., GÃ¶z, F., Akbulut, O.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty},
  abstract = {Correlation analysis is one of the fundamental mathematical tools for identifying dependence between classes. However, the accuracy of the analysis could be jeopardized due to variance error in the data set. This paper provides a mathematical analysis of the impact of imbalanced data concerning Pearson Product Moment Correlation (PPMC) analysis. To alleviate this issue, the novel framework Robust Correlation Analysis Framework (RCAF) is proposed to improve the correlation analysis accuracy. A review of the issues due to imbalanced data and data uncertainty in machine learning is given. The proposed framework is tested with in-depth analysis of real-life solar irradiance and weather condition data from Johannesburg, South Africa. Additionally, comparisons of correlation analysis with prominent sampling techniques, i.e., Synthetic Minority Over-Sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) sampling techniques are conducted. Finally, K-Means and Wards Agglomerative hierarchical clustering are performed to study the correlation results. Compared to the traditional PPMC, RCAF can reduce the standard deviation of the correlation coefficient under imbalanced data in the range of 32.5%–93.02%. © 2018 Elsevier Inc.},
  doi = {10.1016/j.ins.2018.08.017},
  journal = {Information Sciences},
  bibtexkey = {Lai201958},
  author = {Lai, C.S., Tao, Y., Xu, F., Ng, W.W.Y., Jia, Y., Yuan, H., Huang, C., Lai, L.L., Xu, Z., Locatelli, G.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Deep Learning Approach for Intelligent Intrusion Detection System},
  abstract = {Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01-0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks. © 2013 IEEE.},
  doi = {10.1109/ACCESS.2019.2895334},
  journal = {IEEE Access},
  bibtexkey = {Vinayakumar201941525},
  author = {Vinayakumar, R., Alazab, M., Soman, K.P., Poornachandran, P., Al-Nemrat, A., Venkatraman, S.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {An efficient preprocessing method for supervised sentiment analysis by converting sentences to numerical vectors: a twitter case study},
  abstract = {Along with the significant growth of social media, individuals and companies are increasingly receiving public opinions which direct their decisions. Opinion mining, which is considered as a sub-field of natural language processing, information retrieval, and text mining, is the process of understanding the users’ views from their comment, which have been represented as unstructured texts. Emergence of online social media has led to the production of a huge amount of user comments on websites, and thus, has raised opinion mining as a very useful and challenging problem. In this paper, an efficient preprocessing method for opinion mining is presented and will be used for analyzing users’ comments on Twitter social network. For this purpose, different text preprocessing techniques have been used on the dataset to achieve an acceptable standard text. Word2vec method which is a fast and accurate method have been also exploited to convert the words’ arrays to numerical vectors. Machine learning methods, with supervised learning approach, have been applied on the obtained data after this fast and accurate preprocessing phase. Python and RapidMiner have been used to implement different opinion mining methods and the results of these implementations have been compared and evaluated. The experimental results show that the combined use of the preprocessing method of this paper and support vector machine and artificial neural network have the highest accuracy compared to other methods. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.},
  doi = {10.1007/s11042-019-7586-4},
  journal = {Multimedia Tools and Applications},
  bibtexkey = {Sohrabi2019},
  author = {Sohrabi, M.K., Hemmatian, F.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Vibration Test of Single Coal Gangue Particle Directly Impacting the Metal Plate and the Study of Coal Gangue Recognition Based on Vibration Signal and Stacking Integration},
  abstract = {In order to realize the recognition of coal gangue in the top coal caving process, a scheme of the coal gangue recognition based on the collision vibration signal between coal gangue and the metal plate is proposed in this paper, a systematic and standardized impacting test between coal gangue particles and the metal plate is designed for the first time, the vibration signal standardized processing method by the signal intercepting and the coal gangue impact vibration signal recognition algorithm by stacking integration are innovatively proposed. First, a single particle impact on the metal plate test-bed was designed and constructed. Then 1,000 groups coal and 1,000 groups gangue impact on the metal plate tests were carried out respectively, and the vibration acceleration signals of the metal plate were collected. After that, through the signal intercepting, calculating the time-domain characteristics and HHT processing of the vibration signal, 10 time-frequency characteristics, such as the variance of the intercepted signal and the Hilbert marginal spectrum energy value, are determined to form the feature vector. Finally, based on the two different type of the signal samples, the intercepted signal feature vector, and the original intercepted signal, coal gangue recognition by the seven machine learning algorithms, including the decision tree (DT), random forest (RF), XGBoost, long short-term memory (LSTM), support vector machine (SVM), factorization machine (FM), and stacking integration is carried out respectively, and the basis for selecting recognition schemes is discussed. The results show that the coal gangue recognition rate with the same recognition algorithm by using the intercepted signal samples is higher than that of the feature vector samples, the Staking integration algorithm based on the same sample has the highest recognition rate, and the Staking integration algorithm based on the feature vector has the most significant comprehensive advantage in top coal caving process. © 2013 IEEE.},
  doi = {10.1109/ACCESS.2019.2932118},
  journal = {IEEE Access},
  bibtexkey = {Yang2019106784},
  author = {Yang, Y., Zeng, Q., Yin, G., Wan, L.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Behavioral attributes and financial churn prediction},
  abstract = {Customer retention is crucial in a variety of businesses as acquiring new customers is often more costly than keeping the current ones. As a consequence, churn prediction has attracted great attention from both the business and academic worlds. Traditional efforts in the financial domain mainly focus on domain specific variables such as product ownership or service usage aggregation, however, without considering dynamic behavioral patterns of customers’ financial transactions. In this paper, we attempt to fill in this gap by investigating the spatio-temporal patterns and entropy of choices underlying the customers’ financial decisions, and their relations to customer churning activities. Inspired by previous works in the emerging field of computational social science, we built a prediction model based on spatio-temporal and choice behavioral traits using individual transaction records. Our results show that proposed dynamic behavioral models could predict churn decisions significantly better than traditionally considered factors such as demographic-based features, and that this effect remains consistent across multiple data sets and various churn definitions. We further study the relative importance of the various behavioral features in churn prediction, and how the predictive power varies across different demographic groups. More generally, the proposed features can also be applied to churn prediction in other domains where spatio-temporal behavioral data are available. © 2018, The Author(s).},
  doi = {10.1140/epjds/s13688-018-0165-5},
  journal = {EPJ Data Science},
  bibtexkey = {Kaya2018},
  author = {Kaya, E., Dong, X., Suhara, Y., Balcisoy, S., Bozkaya, B., Pentland, A.S.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Personality, User Preferences and Behavior in Recommender systems},
  abstract = {This paper reports on a study of 1840 users of the MovieLens recommender system with identified Big-5 personality types. Based on prior literature that suggests that personality type is a stable predictor of user preferences and behavior, we examine factors of user retention and engagement, content preferences, and rating patterns to identify recommender-system related behaviors and preferences that correlate with user personality. We find that personality traits correlate significantly with behaviors and preferences such as newcomer retention, intensity of engagement, activity types, item categories, consumption versus contribution, and rating patterns. © 2017, Springer Science+Business Media, LLC.},
  doi = {10.1007/s10796-017-9800-0},
  journal = {Information Systems Frontiers},
  bibtexkey = {Karumur20181241},
  author = {Karumur, R.P., Nguyen, T.T., Konstan, J.A.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2018},
  title = {Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning},
  abstract = {Customer churn refers to when a customer ceases their relationship with a company. A churn rate, used to estimate growth, is now considered as important a metric as financial profit. With growing competition in the market, companies are desperate to keep the churn rate as low as possible. Thus, churn prediction has gained critical importance, not just for existing customers, but also for predicting trends of future customers. This paper demonstrates prediction of churn on a Telco dataset using a Deep Learning Approach. A multilayered Neural Network was designed to build a non-linear classification model. The churn prediction model works on customer features, support features, usage features and contextual features. The possibility of churn as well as the determining factors are predicted. The trained model then applies the final weights on these features and predict the possibility of churn for that customer. An accuracy of 80.03% was achieved. Since the model also provides the churn factors, it can be used by companies to analyze the reasons for these factors and take steps to eliminate them. © 2018 IEEE.},
  doi = {10.1109/ICSCEE.2018.8538420},
  journal = {2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018},
  bibtexkey = {Agrawal2018},
  author = {Agrawal, S., Das, A., Gaikwad, A., Dhage, S.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2018},
  title = {Forecasting of financial risk users' outflow},
  abstract = {The study is devoted to building new models for forecasting the users' outflow on online platform games which are based on their behavior and strategy in previous games that is used as history statistics. Such models as logistic regression, decision trees, gradient boosting, etc. for online gambling platform were used for calculating the probability of the client's outflow. High quality performance of the models was received for model based on gradient boosting. For evaluating the models the following parameters were used: common accuracy, first and second-types errors, and area under the receiver operation curve. The most effective criterion which combines the possibility to receive high accuracy, sensitivity to real situation and minimize the specificity of the model was the statistic: area under the curve (AUC). It turned out that the Xgboost method makes it much easier to model nonlinear relationships between regressors and the target field, which leads to better and more accurate classification and evaluation of the probability of a client outflow. © 2018 IEEE.},
  doi = {10.1109/SAIC.2018.8516782},
  journal = {2018 IEEE 1st International Conference on System Analysis and Intelligent Computing, SAIC 2018 - Proceedings},
  bibtexkey = {Kuznietsova2018},
  author = {Kuznietsova, N., Bidyuk, P.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {AFSNN: A Classification Algorithm Using Axiomatic Fuzzy Sets and Neural Networks},
  abstract = {In this study, we present a comprehensible classifier AFSNN that embeds a new type of coherence membership function, which builds upon the theoretical findings of the axiomatic fuzzy set (AFS) theory into the hidden layer of neural network with random weights (NNRWs). Borrowing from the idea of NNRWs that employs the random initialization technique, the relation among attributes, simple concepts, and complex concepts are randomly determined. Complex concepts are generated through the combination of randomly selected simple concepts by AFS logic operation. The output weights of NNRWs are utilized to evaluate the confidence of each complex concept for every target class, which means that the feasibility of complex concepts for every class is determined analytically rather than through the tuning parameters of constraint conditions such as in conventional AFS-based classifiers. For the proposed method, compared to other neural-network-based classification methods, the fuzzy descriptions generated from complex concepts in hidden layer make classification result human understandable. We have experimented with several benchmark datasets and compared the results with other neural network-based classifiers. We show that our method outperforms Ensemble, EvRBFN, NNEP, LVQ, and iRProp+ in the seven out of ten datasets. The results show that the performance of AFSNN is competitive in terms of classification accuracy and the network shows a distinctive capability of providing explicit knowledge in the form of linguistic description. © 1993-2012 IEEE.},
  doi = {10.1109/TFUZZ.2017.2788875},
  journal = {IEEE Transactions on Fuzzy Systems},
  bibtexkey = {Duan20183151},
  author = {Duan, X., Wang, Y., Pedrycz, W., Liu, X., Wang, C., Li, Z.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {A random coefficients mixture hidden Markov model for marketing research},
  abstract = {The hidden Markov model (HMM) provides a framework to model the time-varying effects of marketing mix variables. When employed in a panel data context, it is important to properly account for unobserved heterogeneity across individuals. We propose a new random coefficients mixture HMM (RCMHMM) that allows for flexible patterns of unobserved heterogeneity in both the state-dependent and transition parameters. The RCMHMM nests all HMMs found in the marketing literature. Results of two simulation studies demonstrate that 1) averaging across a large number of different data generating processes, the RCMHMM outperforms all its nested versions using both in-sample and out-of-sample performance and 2) the RCMHMM is more robust than its nested versions when underlying model assumptions are violated. In addition, we apply the RCMHMM to an empirical application where we examine the effectiveness of in-game promotions in increasing the short-term demand for Major League Baseball (MLB) attendance. We find that the effectiveness of four promotional categories varies over the course of the season and across teams and that the RCMHMM performs best. © 2018 Elsevier B.V.},
  doi = {10.1016/j.ijresmar.2018.07.002},
  journal = {International Journal of Research in Marketing},
  bibtexkey = {Kappe2018415},
  author = {Kappe, E., Stadler Blank, A., DeSarbo, W.S.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Predicting interpurchase time in a retail environment using customer-product networks: An empirical study and evaluation},
  abstract = {In predictive analytics and statistics, entities are frequently treated as individual actors. However, in reality this assumption is not valid. In the context of retail, similar customers will behave and thus also purchase similarly to each other. By combining their behavior in an intelligent way, based on transaction history, we can leverage these connections and improve our ability to predict purchase outcomes. As such, we can create customer-product networks from which we can deduce information on customers expressing similar purchasing behavior. This allows us to exploit their preferences and predict which products are going to be sold significantly less often. We want to use this information mainly for gaining novel marketing insights on products. For example, if customers refrain from buying products this might be due to contextual reasons such as new complements or supplements, or new nearby shops. By using these networks on data from an offline European retail corporation, we are able to boost performance of the predictive models by 6% and the identification of these specific products by 20%. This indicates that the development of customer-product graphs in retail can lead to improved marketing intelligence. To our knowledge, this is one of the first studies to use customer-product networks for predictive modeling in an offline retail setting. Furthermore, we suggest an extensive set of product and network features which can guide future researchers and practitioners in their model development. © 2018 Elsevier Ltd},
  doi = {10.1016/j.eswa.2018.03.016},
  journal = {Expert Systems with Applications},
  bibtexkey = {Lismont201822},
  author = {Lismont, J., Ram, S., Vanthienen, J., Lemahieu, W., Baesens, B.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Clustering prediction techniques in defining and predicting customers defection: The case of e-commerce context},
  abstract = {With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several e-commerce sites and compare their competitors" products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-Recency-Frequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macro-averaging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers" performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection. Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved.},
  doi = {10.11591/ijece.v8i4.pp2367-2383},
  journal = {International Journal of Electrical and Computer Engineering},
  bibtexkey = {Rachid20182367},
  author = {Rachid, A.D., Abdellah, A., Belaid, B., Rachid, L.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Multi Criteria Decision Making in Financial Risk Management with a Multi-objective Genetic Algorithm},
  abstract = {A huge amount of data is being collected and stored by financial institutions like banks during their operations. These data contain the most important facts about the institutions and its customers. A good and efficient data analytics system can find patterns in this huge data source that can be used in actionable knowledge creation. Actionable knowledge is the knowledge that can be put to decision making and take some positive action towards better performance of organizations. This actionable knowledge is termed Business Intelligence by data scientists. Business Intelligence and Analytics is the process of applying data mining techniques to organizational or corporate data to discover patterns. Business Intelligence and Business Analytics are emerging as important and essential fields both for data scientists and organizations. Risk analysis, fraud detection, customer retention, customer satisfaction analysis and actuarial analysis are some of the areas of application of business intelligence and analytics. Credit risk analysis is an important part of a successful financial institution particularly in the banking sector. The current study takes this risk analysis in financial institutions and reviews the state of the art in using data analytics or data mining techniques for financial risk analysis. The analysis of risk from financial data depends on several factors that are both objective and subjective. Hence it is a multi-criteria decision problem. The study also proposes a multi-objective genetic algorithm (MOGA) for analyzing financial data for risk analysis and prediction. The proposed MOGA is different from other evolutionary systems in that a memory component to hold the rules is added to the system while other systems in the literature are memory less. The algorithm is applied to bench mark data sets for predicting the decision on credit card and credit applications. The preliminary results are encouraging and show light towards better decision making in reducing risks. © 2017, Springer Science+Business Media New York.},
  doi = {10.1007/s10614-017-9683-7},
  journal = {Computational Economics},
  bibtexkey = {Srinivasan2018443},
  author = {Srinivasan, S., Kamalakannan, T.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {A novel knowledge-leverage-based transfer learning algorithm},
  abstract = {A major assumption in traditional machine leaning is that the training and testing data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. In recent years, transfer learning has emerged as a new learning paradigm to cope with this considerable challenge. It focuses on exploiting previously learnt knowledge by leveraging information from an old source domain to help learning in a new target domain. In this work, we integrate the knowledge-leverage-based Transfer Learning mechanism with a Rank-based Reduce Error ensemble selection approach to fulfill the transfer learning task, called RankRE-TL. Ensemble selection is important for improving both efficiency and predictive accuracy of an ensemble system. It aims to select a proper subset of the whole ensemble, which usually outperforms the whole one. Therefore, we appropriately modify the Reduce Error (RE) pruning technique and design a new Rank-based Reduce Error ensemble selection method (RankRE) to deal with the transfer learning task. The design idea of RankRE is to find the candidate classifier which is expected to improve the classification performance of the extended subensemble the most. In the RankRE-TL algorithm, the initial Support Vector Machine (SVM) ensemble is learnt based upon dynamic training dataset regrouping. And simultaneously, a new construction method of validation set is designed for RankRE-TL, which differs from the method used in conventional ensemble selection paradigm. © 2017, Springer Science+Business Media, LLC.},
  doi = {10.1007/s10489-017-1084-z},
  journal = {Applied Intelligence},
  bibtexkey = {Li20182355},
  author = {Li, M., Dai, Q.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2018},
  title = {Multiple feature inputs support vector regression},
  abstract = {While traditional support vector regression algorithm is based on a single kernel, in practice it is often desirable to use multiple kernels. Learning linear combinations of multiple kernels is an appealing strategy when the right choice of feature is unknown. A new support vector regression framework for multiple feature inputs learning problem is proposed here, which mapped the different feature inputs with different nonlinear functions into high dimensional feature space, and resolved as a semi-infinite linear program. Experimental results show that the proposed algorithm works well, and helps to improve the accuracy and the generalization for regression problems with multiple feature inputs. © 2018 IEEE.},
  doi = {10.1109/CCDC.2018.8407338},
  journal = {Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018},
  bibtexkey = {Yuan20181352},
  author = {Yuan, C., Xu, S.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2018},
  title = {An innovative optimized model to anticipate clients about immigration in telecom industry},
  abstract = {This article proposed an innovative model to predict churning and non churning of clients in the telecom industry. Now-a-days, telecom customers are frequently migrating from one network to the other due to various constraints, policies and standards in public and private sectors. Usually in current industry the cost to retain the existing clients is smaller than getting a pioneering customer. To survive in the current competitive world, there is a need to design an optimal prediction model for churning and non churning of telecom clients. The proposed model has been outperformed with an accuracy level of 99.61% than existing models and techniques. Earlier authors have achieved 94.03 % of accuracy using machine learning techniques to predict churning of customers. © 2017 IEEE.},
  doi = {10.1109/ICATCCT.2017.8389139},
  journal = {Proceedings of the 2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2017},
  bibtexkey = {Naik2018232},
  author = {Naik, M.V., Reddy, S.S.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {On the operational efficiency of different feature types for telco Churn prediction},
  abstract = {Churn prediction in telco remains a very active research topic. Due to the uptake of social network analytics and the results of previous benchmarking studies showing a rather flat maximum performance effect of predictive modeling techniques, the focus has mainly shifted to expanding and exploring the relevant feature space. While previous studies generally agree that adding features typically increases predictive performance, they rarely discuss the accompanying issues such as data availability and computational cost. In this work, we bridge the gap between predictive performance and operational efficiency by devising a new feature type classification and a novel reusable method to determine optimal feature type combinations based on Pareto multi-criteria optimization. Our results provide several insights that can serve as a guideline for industry practitioners. © 2017 Elsevier B.V.},
  doi = {10.1016/j.ejor.2017.12.015},
  journal = {European Journal of Operational Research},
  bibtexkey = {MitroviÄ‡20181141},
  author = {MitroviÄ‡, S., Baesens, B., Lemahieu, W., De Weerdt, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {A New Fuzzy Modeling Framework for Integrated Risk Prognosis and Therapy of Bladder Cancer Patients},
  abstract = {This paper presents a new fuzzy modeling approach for analyzing censored survival data and finding risk groups of patients diagnosed with bladder cancer. The proposed framework involves a new procedure for integrating the frameworks of interval type-2 fuzzy logic and Cox modeling intrinsically. The output of this synergistic framework is a score/prognostics index which is indicative of the patient's level of mortality risk. A threshold value is selected whereby patients with risk scores that are greater than this threshold are classed as high-risk patients and vice versa. Unlike in the case of black-box type modeling approaches, the paper shows that interpretability and transparency are maintained using the proposed fuzzy modeling framework. Two datasets are used to test the modeling accuracy of the elicited models. The first is an artificial dataset which has similar characteristics as in a typical survival data. The second relates to real-life bladder cancer data from which one requires a model that identifies the low-risk and high-risk patients and then recommends risk management decisions based on, predicted risk level, patient history and characteristics, disease pathology, and event times. The performance of the proposed framework is compared with the traditional Cox model, logistic regression as well as a nonlinear survival data modeling technique based on neural networks. This is the first time an attempt has been made to exploit the transparency advantages of fuzzy models and the principled statistical framework of the Cox model in order to identify risk groups and recommend risk management decisions from complex survival datasets. In both the artificial data and real data, the proposed modeling framework, although minimalistic, shows better generalization performances than the previously reported models against which the results were compared. © 1993-2012 IEEE.},
  doi = {10.1109/TFUZZ.2017.2735939},
  journal = {IEEE Transactions on Fuzzy Systems},
  bibtexkey = {Obajemu20181565},
  author = {Obajemu, O., Mahfouf, M., Catto, J.W.F.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Interactive evolutionary approaches to multiobjective feature selection},
  abstract = {In feature selection problems, the aim is to select a subset of features to characterize an output of interest. In characterizing an output, we may want to consider multiple objectives such as maximizing classification performance, minimizing number of selected features or cost, etc. We develop a preference-based approach for multiobjective feature selection problems. Finding all Pareto-optimal subsets may turn out to be a computationally demanding problem and we still would need to select a solution. Therefore, we develop interactive evolutionary approaches that aim to converge to a subset that is highly preferred by the decision maker (DM). We test our approaches on several instances simulating DM preferences by underlying preference functions and demonstrate that they work well. © 2017 The Authors. International Transactions in Operational Research © 2017 International Federation of Operational Research Societies},
  doi = {10.1111/itor.12428},
  journal = {International Transactions in Operational Research},
  bibtexkey = {Ã–zmen20181027},
  author = {Ã–zmen, M., Karakaya, G., KÃ¶ksalan, M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Advanced Customer Analytics: Strategic Value Through Integration of Relationship-Oriented Big Data},
  abstract = {As more firms adopt big data analytics to better understand their customers and differentiate their offerings from competitors, it becomes increasingly difficult to generate strategic value from isolated and unfocused ad hoc initiatives. To attain sustainable competitive advantage from big data, firms must achieve agility in combining rich data across the organization to deploy analytics that sense and respond to customers in a dynamic environment. A key challenge in achieving this agility lies in the identification, collection, and integration of data across functional silos both within and outside the organization. Because it is infeasible to systematically integrate all available data, managers need guidance in finding which data can provide valuable and actionable insights about customers. Leveraging relationship marketing theory, we develop a framework for identifying and evaluating various sources of big data in order to create a value-justified data infrastructure that enables focused and agile deployment of advanced customer analytics. Such analytics move beyond siloed transactional customer analytics approaches of the past and incorporate a variety of rich, relationship-oriented constructs to provide actionable and valuable insights. We develop a customized kernel-based learning method to take advantage of these rich constructs and instantiate the framework in a novel prototype system that accurately predicts a variety of customer behaviors in a challenging environment, demonstrating the framework’s ability to drive significant value. Copyright © Taylor & Francis Group, LLC.},
  doi = {10.1080/07421222.2018.1451957},
  journal = {Journal of Management Information Systems},
  bibtexkey = {Kitchens2018540},
  author = {Kitchens, B., Dobolyi, D., Li, J., Abbasi, A.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {A Novel Greedy Randomized Dynamic Ensemble Selection Algorithm},
  abstract = {This work proposes a novel greedy randomized dynamic ensemble selection (DES) algorithm combined with path-relinking, variable neighborhood search (VNS) and greedy randomized adaptive search procedure (GRASP) algorithms, abbreviated DyPReVNsGraspEnS. Instead of simply selecting the classifiers with the best competence under certain criterions, DyPReVNsGraspEnS makes greedy randomized selection of appropriate classifiers to form the ensemble. It realizes random multi-start search, is capable to easily escape from the local optimums, and possesses a high probability to find the optimal subensemble. It effectively strengthens the link between iterations and solves the problem of no memory by integrating the path-relinking technique. Moreover, the algorithm properly extends the neighborhood with the help of VNS, having a higher competence of global optimization. Most important of all, DyPReVNsGraspEnS is developed based upon the framework of DES paradigm, therefore, it inherits numerous advantages of DES. Empirical investigations are conducted based on twelve benchmark datasets, including six real-world credit/finance datasets and six datasets drawn from other different fields with more than two classes. To clearly articulate the performance of each strategy in the proposed algorithm, experiments for the incorporation of different strategies are implemented separately on all the datasets. The experimental results demonstrate that, in comparison with other classical ensemble learning techniques, DyPReVNsGraspEnS achieves significantly superior generalization performance. And it can not only be used for regions like credit risk assessment, but is applicable to much broader application fields as well. © 2017, Springer Science+Business Media, LLC.},
  doi = {10.1007/s11063-017-9670-y},
  journal = {Neural Processing Letters},
  bibtexkey = {Ye2018565},
  author = {Ye, R., Dai, Q.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values},
  abstract = {The goal of customer retention campaigns, by design, is to add value and enhance the operational efficiency of businesses. For organizations that strive to retain their customers in saturated, and sometimes fast moving, markets such as the telecommunication and banking industries, implementing customer churn prediction models that perform well and in accordance with the business goals is vital. The expected maximum profit (EMP) measure is tailored toward this problem by taking into account the costs and benefits of a retention campaign and estimating its worth for the organization. Unfortunately, the measure assumes fixed and equal customer lifetime value (CLV) for all customers, which has been shown to not correspond well with reality. In this article, we extend the EMP measure to take into account the variability in the lifetime values of customers, thereby basing it on individual characteristics. We demonstrate how to incorporate the heterogeneity of CLVs when CLVs are known, when their prior distribution is known, and when neither is known. By taking into account individual CLVs, our proposed approach of measuring model performance gives novel insights when deciding on a customer retention campaign. The method is dependent on the characteristics of the customer base as is compliant with modern business analytics and accommodates the data-driven culture that has manifested itself within organizations. © Copyright 2018, Mary Ann Liebert, Inc. 2018.},
  doi = {10.1089/big.2018.0015},
  journal = {Big Data},
  bibtexkey = {Ã“skarsdÃ³ttir201853},
  author = {Ã“skarsdÃ³ttir, M., Baesens, B., Vanthienen, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Retention futility: Targeting high-risk customers might be ineffective},
  abstract = {Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models that identify customers at the highest risk of churning, no research has investigated whether it is indeed optimal to target those individuals. Combining two field experiments with machine learning techniques, the author demonstrates that customers identified as having the highest risk of churning are not necessarily the best targets for proactive churn programs. This finding is not only contrary to common wisdom but also suggests that retention programs are sometimes futile not because firms offer the wrong incentives but because they do not apply the right targeting rules. Accordingly, firms should focus their modeling efforts on identifying the observed heterogeneity in response to the intervention and to target customers on the basis of their sensitivity to the intervention, regardless of their risk of churning. This approach is empirically demonstrated to be significantly more effective than the standard practice of targeting customers with the highest risk of churning. More broadly, the author encourages firms and researchers using randomized trials (or A/B tests) to look beyond the average effect of interventions and leverage the observed heterogeneity in customers' response to select customer targets. © 2018, American Marketing Association.},
  doi = {10.1509/jmr.16.0163},
  journal = {Journal of Marketing Research},
  bibtexkey = {Ascarza201880},
  author = {Ascarza, E.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {A Robust profit measure for binary classification model evaluation},
  abstract = {Using profit-based evaluation measures is a necessity in business-oriented contexts, as they aid companies in making cost-optimal decisions. Among the measures that effectively include the true nature of costs and benefits in binary classification, the expected maximum profit (EMP) has been used successfully for churn prediction and credit scoring, and defined in general for binary classification problems. However, despite its competitive results against the most frequently used measures, the EMP relies on a fixed probability distribution of costs and benefits, the range of which in real applications is not entirely known. In this paper, we propose to extend this measure by adding random shocks to these distributions. We call this new measure the R-EMP, following the convention of the analogous EMP measure. Our metric adds a stochastic component to each point of the cost-benefit distributions, assuming that costs and benefits have a fixed probability, but its distribution range is subject to an external shock, which can be different for each cost or benefit. The experimental set-up is focused on a credit scoring application using a dataset of a Chilean financial institution, with the attribute selection for a logistic regression being accomplished using the AUC, EMP, H-measure, and R-EMP as the selection criteria. The results indicate that the R-EMP measure is the most robust metric for achieving the greatest profit for the company under uncertain external conditions. © 2017 Elsevier Ltd},
  doi = {10.1016/j.eswa.2017.09.045},
  journal = {Expert Systems with Applications},
  bibtexkey = {Garrido2018154},
  author = {Garrido, F., Verbeke, W., Bravo, C.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2018},
  title = {To be or not to be... social: Incorporating simple social features in mobile game customer lifetime value predictions},
  abstract = {Mobile games make up the largest segment of the games industry, in terms of revenue as well as players. Hundreds of thousands of games are available with most being free to download and play. In freemium games, revenue is predominantly generated by users making in-game purchases. As only a small fraction of users make purchases, predicting these users and their Customer Lifetime Value are key challenges in Game Analytics and currently barely explored in academic research. Furthermore, while social factors have been shown to be essential for retention in games in general, the impact on retention and monetization in mobile games is unexplored. In this paper, the problem of defining social features in freemium casual mobile games is addressed through a case study with over 200,000 players. .e study evaluates the in.uence of specific types of social interactions typical of casual mobile games, on predictions of premium users and Customer Lifetime Value by applying classifiers and regression models respectively. Results indicate that social activity does not correlate with the tendency to become a premium user, but that social activity increases over time in a cohort. © 2018 ACM.},
  doi = {10.1145/3167918.3167925},
  journal = {ACM International Conference Proceeding Series},
  bibtexkey = {Drachen2018},
  author = {Drachen, A., Pastor, M., Liu, A., Fontaine, D.J., Chang, Y., Runge, J., Sifa, R., Klabjan, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Multiagent-consensus-MapReduce-based attribute reduction using co-evolutionary quantum PSO for big data applications},
  abstract = {The attribute reduction for big data applications has become an urgent challenge in pattern recognition, machine learning and data mining. In this paper, we introduce the multi-agent consensus MapReduce optimization model and co-evolutionary quantum PSO with self-adaptive memeplexes for designing the attribute reduction method, and propose a multiagent-consensus-MapReduce-based attribute reduction algorithm (MCMAR). Firstly, the co-evolutionary quantum PSO with self-adaptive memeplexes is designed for grouping particles into different memeplexes, which aims to explore the search space and locate the global best region during the attribute reduction of big datasets. Secondly, the four layers neighborhood radius framework with compensatory scheme is constructed to partition big attribute sets by exploiting the interdependency among multiple-relevant-attribute sets. Thirdly, a novel multi-agent consensus MapReduce optimization model is adopted to perform the multiple-relevance-attribute reduction, in which five kinds of agents are used to conduct the ensemble co-evolutionary optimization. So the uniform reduction framework of different agents’ co-evolutionary game under the bounded rationality is further refined. Fourthly, the approximation MapReduce parallelism mechanism is permitted to formalize to the multi-agent co-evolutionary consensus structure, interaction and adaptation, which enhances different agents to share their solutions. Finally, extensive experimental studies substantiate the effectiveness and accuracy of MCMAR on some well-known benchmark datasets. Moreover, successful applications in big medical datasets are expected to dramatically scaling up MCMAR for complex infant brain MRI in terms of efficiency and feasibility. © 2017 Elsevier B.V.},
  doi = {10.1016/j.neucom.2017.06.059},
  journal = {Neurocomputing},
  bibtexkey = {Ding2018136},
  author = {Ding, W., Lin, C.-T., Chen, S., Zhang, X., Hu, B.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Benchmarking sampling techniques for imbalance learning in churn prediction},
  abstract = {Class imbalance presents significant challenges to customer churn prediction. Many data-level sampling solutions have been developed to deal with this issue. In this paper, we comprehensively compare the performance of several state-of-the-art sampling techniques in the context of churn prediction. A recently developed maximum profit criterion is used as one of the main performance measures to offer more insights from the perspective of cost-benefit. The experimental results show that the impact of sampling methods depends on the used evaluation metric and that the impact pattern is interrelated with the classifiers. An in-depth exploration of the reaction patterns is conducted, and suitable sampling strategies are recommended for each situation. Furthermore, we also discuss the setting of the sampling rate in the empirical comparison. Our findings will offer a useful guideline for the use of sampling methods in the context of churn prediction. © 2017 Operational Research Society.},
  doi = {10.1057/s41274-016-0176-1},
  journal = {Journal of the Operational Research Society},
  bibtexkey = {Zhu201849},
  author = {Zhu, B., Baesens, B., Backiel, A., Vanden Broucke, S.K.L.M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Herding by Foreign Institutional Investors: An Evidential Exploration for Persistence and Predictability},
  abstract = {The primary objective of the study is to explore the predictability of herding patterns of foreign institutional investors in the Indian market using high frequency data over a period from January 2003 to June 2014. Herding of an individual stock was measured estimating a simple volume based ratio and persistence of trends was detected using the runs test (Wald and Wolfowitz [1940]) on that ratio. Predictability of herding behavior has been successfully modeled by applying 7 data mining models using various measures of performance. Market regulators may consider our findings to regulate the foreign institutional investors trading to make the financial system more transparent and robust. © 2018 The Institute of Behavioral Finance.},
  doi = {10.1080/15427560.2017.1373282},
  journal = {Journal of Behavioral Finance},
  bibtexkey = {Chattopadhyay201873},
  author = {Chattopadhyay, M., Garg, A.K., Mitra, S.K.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2018},
  title = {Churn and non-churn of customers in banking sector using extreme learning machine},
  abstract = {With an invention been made in computational techniques, there has been increasing interest in the field of neural networks as major potential machine learning techniques have come to the front. In the current past, gradient descent-based algorithm is used in feed forward neural network and the parameter utilized, which devours extensively more opportunity for learning and tuned iteratively. The single hidden layer feed forward neural network have the input weights, the hidden layer and biases randomly assigned. The straightforward backward operation is utilized to discover output weight which thus relies on upon handling from hidden layer to output layer. In this paper, we propose to utilize Extreme Learning Machine (ELM) to foresee client churn. The goal of this paper is to propose a novel approach that enhances the precision of churn and non-churn of clients using banking data. © 2018, Springer Nature Singapore Pte Ltd.},
  doi = {10.1007/978-981-10-8228-3_6},
  journal = {Advances in Intelligent Systems and Computing},
  bibtexkey = {Mohanty201851},
  author = {Mohanty, R., Naga Ratna Sree, C.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {A stability constrained adaptive alpha for gravitational search algorithm},
  abstract = {Gravitational search algorithm (GSA), a recent meta-heuristic algorithm inspired by Newton's law of gravity and mass interactions, shows good performance in various optimization problems. In GSA, the gravitational constant attenuation factor alpha (a) plays a vital role in convergence and the balance between exploration and exploitation. However, in GSA and most of its variants, all agents share the same a value without considering their evolutionary states, which has inevitably caused the premature convergence and imbalance of exploration and exploitation. In order to alleviate these drawbacks, in this paper, we propose a new variant of GSA, namely stability constrained adaptive alpha for GSA (SCAA). In SCAA, each agent's evolutionary state is estimated, which is then combined with the variation of the agent's position and fitness feedback to adaptively adjust the value of a. Moreover, to preserve agents’ stable trajectories and improve convergence precision, a boundary constraint is derived from the stability conditions of GSA to restrict the value of a in each iteration. The performance of SCAA has been evaluated by comparing with the original GSA and four alpha adjusting algorithms on 13 conventional functions and 15 complex CEC2015 functions. The experimental results have demonstrated that SCAA has significantly better searching performance than its peers do. © 2017},
  doi = {10.1016/j.knosys.2017.10.018},
  journal = {Knowledge-Based Systems},
  bibtexkey = {Sun2018200},
  author = {Sun, G., Ma, P., Ren, J., Zhang, A., Jia, X.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Identifying soccer players on facebook through predictive analytics},
  abstract = {This study assesses the feasibility of identifying self-reported sports practitioners (soccer players) on Facebook. The main goal is to develop a system to support marketers with the decision as to which prospects to target for advertising purposes. To do so, we benchmark several algorithms (i.e., random forest, logistic regression, adaboost, rotation forest, neural networks, and kernel factory) using five times twofold cross-validation. To evaluate performance and variable importances, we build a fusion model, which combines the results of the other algorithms using the weighted average. This technique is also referred to as information-fusion sensitivity analysis. The results reveal that Facebook data provide a viable basis to come up with sports predictions as the predictive performance ranges from 72.01% to 80.43% for area under the receiver operating characteristic curve (AUC), from 81.96% to 83.95% for accuracy, and from 2.41 to 3.06 for top-decile lift. Our benchmark study indicates that stochastic adaboost, the fusion model, random forest, rotation forest, and regularized logistic regression are the best-performing algorithms. Furthermore, the results show that the most important variables are the average number of friends that play soccer, membership of a soccer group, and the number of favorite teams. We also assess the impact of our results on profitability by conducting a thorough sensitivity analysis. Our analysis reveals that our approach can be beneficial for a wide range of companies. The analysis and results in this study will assist sports brands with decisions regarding their implementation of targeted marketing approaches. © 2017 INFORMS.},
  doi = {10.1287/deca.2017.0354},
  journal = {Decision Analysis},
  bibtexkey = {Bogaert2017274},
  author = {Bogaert, M., Ballings, M., Hosten, M., Van Den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Personalized air travel prediction: A multi-factor perspective},
  abstract = {Human mobility analysis is one of the most important research problems in the field of urban computing. Existing research mainly focuses on the intra-city ground travel behavior modeling, while the inter-city air travel behavior modeling has been largely ignored. Actually, the inter-city travel analysis can be of equivalent importance and complementary to the intra-city travel analysis. Understanding massive passenger-airtravel behavior delivers intelligence for airlines' precision marketing and related socioeconomic activities, such as airport planning, emergency management, local transportation planning, and tourism-related businesses. Moreover, it provides opportunities to study the characteristics of cities and the mutual relationships between them. However, modeling and predicting air traveler behavior is challenging due to the complex factors of the market situation and individual characteristics of customers (e.g., airlines' market share, customer membership, and travelers' intrinsic interests on destinations). To this end, in this article, we present a systematic study on the personalized air travel prediction problem, namely where a customer will fly to and which airline carrier to fly with, by leveraging real-world anonymized Passenger Name Record (PNR) data. Specifically, we first propose a relational travel topic model, which combines the merits of latent factor model with a neighborhood-based method, to uncover the personal travel preferences of aviation customers and the latent travel topics of air routes and airline carriers simultaneously. Then we present a multi-factor travel prediction framework, which fuses complex factors of the market situation and individual characteristics of customers, to predict airline customers' personalized travel demands. Experimental results on two real-world PNR datasets demonstrate the effectiveness of our approach on both travel topic discovery and customer travel prediction. © 2017 ACM.},
  doi = {10.1145/3078845},
  journal = {ACM Transactions on Intelligent Systems and Technology},
  bibtexkey = {Liu2017},
  author = {Liu, J., Liu, B., Liu, Y., Chen, H., Feng, L., Xiong, H., Huang, Y.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Challenges in the Analysis of Online Social Networks: A Data Collection Tool Perspective},
  abstract = {The present era of internet has radically changed the way people communicate with each other. Online Social Network platforms have enhanced this to real-time communication where interactions vary from casual relationships to formal bonding. This real-time communication between the users over Online Social Network platforms generates data which directly or indirectly gives lot of information. But extracting this data and mining information out of it is a profound challenge. Researchers need appropriate tools to churn out this data and get valuable information by analyzing and visualizing it. This paper does a comprehensive survey of types of Online Social Network Analysis resulting in segregation of research challenges associated with each of the types. A detailed study of the existing data collection tools and analysis techniques was further carried out to understand the challenges a researcher faces while using it. Finally, mapping analysis was done using research challenges, data collection tools and the types of Online Social Network Analysis, to understand to what extent the existing data collection tools and analysis techniques can meet the research challenges. The mapping analysis shows an absolute requirement of new data collection tools and algorithms by the researchers/developers. © 2017, Springer Science+Business Media, LLC.},
  doi = {10.1007/s11277-017-4712-3},
  journal = {Wireless Personal Communications},
  bibtexkey = {Goswami20174015},
  author = {Goswami, A., Kumar, A.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Comparison of deep learning algorithms to predict customer churn within a local retail industry},
  abstract = {A top priority in any business is a constant need to increase revenue and profitability. One of the causes for a decrease in profits is when current customers stop transacting. When a customer leaves or churns from a business, the opportunity for potential sales or cross selling is lost. If a customer leaves the business without any form of advice, the company may find it hard to respond and take corrective action. Ideally companies should adopt a proactive and identify potential churners prior to them leaving. Customer retention strategies have been noted to be less costly than attracting new customers. Through data available within the Point of Sales (POS) systems, customer transactions may be extracted and their buying patterns may be analysed. This paper demonstrates how through transactional data features are created and may be identified as significant to predict churn within the retail industry. The data provided within this paper pertains to a local supermarket. Therefore, the churners identified and results attained are based on real scenarios. The novelty of this paper is the concept of implementing deep learning algorithms. Convolution Neural Networks and Restricted Boltzmann Machine are the selected deep learning techniques. The Restricted Boltzmann Machine attained the best results that of 83% in predicting customer churn.},
  doi = {10.18178/ijmlc.2017.7.5.634},
  journal = {International Journal of Machine Learning and Computing},
  bibtexkey = {Dingli2017128},
  author = {Dingli, A., Marmara, V., Fournier, N.S.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {GMDH-based semi-supervised feature selection for customer classification},
  abstract = {Data dimension reduction is an important step for customer classification modeling, and feature selection has been a research focus of the data dimension reduction field. This study introduces the group method of data handling (GMDH), puts forward a GMDH-based semi-supervised feature selection (GMDH-SSFS) algorithm, and applies it to customer feature selection. The algorithm can utilize a few samples with class labels L, and a large number of samples without class labels U simultaneously. What is more, it considers the relationship between features and class labels, and that between features during feature selection. The GMDH-SSFS model mainly consists of three stages: 1) Train N basic classification models based on the dataset L with class labels; 2) Label samples selectively in the dataset U without class labels, and add them to L; 3) Train the GMDH neural network based on the new training set L, and select the optimal feature subset FS. Based on an empirical analysis of four customer classification datasets, results suggest that the features selected by the GMDH-SSFS model have a good explainability. Meanwhile, the customer classification performance of the classification model trained by the selected feature subset is superior to that of the models trained by the commonly used Laplacian score (an unsupervised feature selection algorithm), Fisher score (a supervised feature selection algorithm), and the FW-SemiFS and S3VM-FS (two semi-supervised feature selection algorithms). © 2017 Elsevier B.V.},
  doi = {10.1016/j.knosys.2017.06.018},
  journal = {Knowledge-Based Systems},
  bibtexkey = {Xiao2017236},
  author = {Xiao, J., Cao, H., Jiang, X., Gu, X., Xie, L.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2017},
  title = {Partition cost-sensitive CART based on customer value for Telecom customer churn prediction},
  abstract = {Telecom Customer churn prediction is a cost sensitive classification problem. Most of studies regard it as a general classification problem use traditional methods, that the two types of misclassification cost are equal. And, in aspect of cost sensitive classification, there are some researches focused on static cost sensitive situation. In fact, customer value of each customer is different, so misclassification cost of each sample is different. For this problem, we propose the partition cost-sensitive CART model in this paper. According to the experiment based on the real data, it is showed that the method not only obtains a good classification performance, but also reduces the total misclassification costs effectively. © 2017 Technical Committee on Control Theory, CAA.},
  doi = {10.23919/ChiCC.2017.8028259},
  journal = {Chinese Control Conference, CCC},
  bibtexkey = {Wang20175680},
  author = {Wang, C., Li, R., Wang, P., Chen, Z.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {A stochastic comparison of customer classifiers with an application to customer attrition in commercial banking},
  abstract = {The classification of clients is an essential matter in commercial banking, insurance companies, electrical corporations, communication business, etc. Those companies frequently classify their customers by means of the information provided by the so-called classifier. Motivated by the need to compare systems of classification, we introduce a new stochastic order which permits the comparison of classifiers. The stochastic order is analysed in detail, providing characterizations and properties as well as connections with other stochastic orders and other classification systems. Such an order is applied to compare some classifiers used by a Spanish commercial banking to analyse the key problem of customer churn, obtaining conclusive results by means of real databases. Namely, the optimal classifier among them in the new stochastic order is obtained. © 2016 Informa UK Limited, trading as Taylor & Francis Group.},
  doi = {10.1080/03461238.2016.1209549},
  journal = {Scandinavian Actuarial Journal},
  bibtexkey = {LÃ³pez-DÃaz2017606},
  author = {LÃ³pez-DÃaz, M.C., LÃ³pez-DÃaz, M., MartÃnez-FernÃ¡ndez, S.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {A fast density and grid based clustering method for data with arbitrary shapes and noise},
  abstract = {This paper presents a density- and grid- based (DGB) clustering method for categorizing data with arbitrary shapes and noise. As most of the conventional clustering approaches work only with round-shaped clusters, other methods are needed to be explored to proceed classification of clusters with arbitrary shapes. Clustering approach by fast search and find of density peaks and density-based spatial clustering of applications with noise, and so many other methods are reported to be capable of completing this task but are limited by their computation time of mutual distances between points or patterns. Without the calculation of mutual distances, this paper presents an alternative method to fulfill clustering of data with any shape and noise even faster and with more efficiency. It was successfully verified in clustering industrial data (e.g., DNA microarray data) and several benchmark datasets with different kinds of noise. It turned out that the proposed DGB clustering method is more efficient and faster in clustering datasets with any shape than the conventional methods. © 2005-2012 IEEE.},
  doi = {10.1109/TII.2016.2628747},
  journal = {IEEE Transactions on Industrial Informatics},
  bibtexkey = {Wu20171620},
  author = {Wu, B., Wilamowski, B.M.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2017},
  title = {Scalable RFM-Enriched representation learning for churn prediction},
  abstract = {Most of the recent studies on churn prediction in telco utilize social networks built on top of the call (and/or SMS) graphs to derive informative features. However, extracting features from large graphs, especially structural features, is an intricate process both from a methodological and computational perspective. Due to the former, feature extraction in the current literature has mainly been addressed in an ad-hoc and handcrafted manner. Due to the latter, the full potential of the structural information is unexploited. In this work, we incorporate both interaction and structural information by devising two different ways of enriching original graphs with interaction information, delineated by the well-known RFM model. We circumvent the process of extensive manual feature engineering by enriching the networks and improving the scalability of the renowned node2vec approach to learn node representations. The obtained results demonstrate that our enriched network outperforms baseline RFM-based methods. © 2017 IEEE.},
  doi = {10.1109/DSAA.2017.42},
  journal = {Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017},
  bibtexkey = {MitroviÄ‡201779},
  author = {MitroviÄ‡, S., Singh, G., Baesens, B., Lemahieu, W., De Weerdt, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Intelligent data analysis approaches to churn as a business problem: a survey},
  abstract = {Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition and, with it, a growing preoccupation among service-providing companies with creating stronger customer bonds. In this context, anticipating the customer’s intention to abandon the provider, a phenomenon known as churn, becomes a competitive advantage. Such anticipation can be the result of the correct application of information-based knowledge extraction in the form of business analytics. In particular, the use of intelligent data analysis, or data mining, for the analysis of market surveyed information can be of great assistance to churn management. In this paper, we provide a detailed survey of recent applications of business analytics to churn, with a focus on computational intelligence methods. This is preceded by an in-depth discussion of churn within the context of customer continuity management. The survey is structured according to the stages identified as basic for the building of the predictive models of churn, as well as according to the different types of predictive methods employed and the business areas of their application. © 2016, Springer-Verlag London.},
  doi = {10.1007/s10115-016-0995-z},
  journal = {Knowledge and Information Systems},
  bibtexkey = {GarcÃa2017719},
  author = {GarcÃa, D.L., Nebot, Ã€., Vellido, A.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior},
  abstract = {Purpose: This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business. Design/methodology/approach: The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model. Findings: According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models. Research limitations/implications: The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution. Practical implications: Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers. Originality/value: The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine (Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using discriminant analysis for selecting and filtering variables which are important and effective in predicting churners and non-churners, which is not used in previous studies. Therefore, the current study is unique considering the used variables, the method of comparing their accuracy and the method of selecting effective variables. © 2017, © Emerald Publishing Limited.},
  doi = {10.1108/JSIT-10-2016-0061},
  journal = {Journal of Systems and Information Technology},
  bibtexkey = {Khodabandehlou201765},
  author = {Khodabandehlou, S., Zivari Rahman, M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms},
  abstract = {The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person's specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.},
  doi = {10.1515/jaiscr-2017-0004},
  journal = {Journal of Artificial Intelligence and Soft Computing Research},
  bibtexkey = {Almohammadi201747},
  author = {Almohammadi, K., Hagras, H., Alghazzawi, D., Aldabbagh, G.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2016},
  title = {Churn prediction in mobile social games: Towards a complete assessment using survival ensembles},
  abstract = {Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression. © 2016 IEEE.},
  doi = {10.1109/DSAA.2016.84},
  journal = {Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016},
  bibtexkey = {Perianez2016564},
  author = {Perianez, A., Saas, A., Guitart, A., Magne, C.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Optimizing security and quality of service in a Real-time database system using Multi-objective genetic algorithm},
  abstract = {Both network security and quality of service (QoS) consume computational resource of IT system and thus may evidently affect the application services. In the case of limited computational resource, it is important to model the mutual influence between network security and QoS, which can be concurrently optimized in order to provide a better performance under the available computational resource. In this paper, an evaluation model is accordingly presented to describe the mutual influence of network security and QoS, and then a multi-objective genetic algorithm NSGA-II is revised to optimize the multi-objective model. Using the intrinsic information from the target problem, a new crossover approach is designed to further enhance the optimization performance. Simulation results validate that our algorithm can find a set of Pareto-optimal security policies under different network workloads, which can be provided to the potential users as the differentiated security preferences. These obtained Pareto-optimal security policies not only meet the security requirement of the user, but also provide the optimal QoS under the available computational resource. © 2016},
  doi = {10.1016/j.eswa.2016.07.023},
  journal = {Expert Systems with Applications},
  bibtexkey = {Zhao201611},
  author = {Zhao, X., Lin, Q., Chen, J., Wang, X., Yu, J., Ming, Z.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Classification of gene expression data using multiobjective differential evolution},
  abstract = {Gene expression data are usually redundant, and only a subset of them presents distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in bioinformatics. In this paper, a multiobjective binary differential evolution method (MOBDE) is proposed to select a small subset of informative genes relevant to the classification. In the proposed method, firstly, the Fisher-Markov selector is used to choose top features of gene expression data. Secondly, to make differential evolution suitable for the binary problem, a novel binary mutation method is proposed to balance the exploration and exploitation ability. Thirdly, the multiobjective binary differential evolution is proposed by integrating the summation of normalized objectives and diversity selection into the binary differential evolution algorithm. Finally, the MOBDE algorithm is used for feature selection, and support vector machine (SVM) is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effectiveness and efficiency of the algorithm, the proposed method is tested on ten gene expression datasets. Experimental results demonstrate that the proposed method is very effective.},
  doi = {10.3390/en9121061},
  journal = {Energies},
  bibtexkey = {Ma2016},
  author = {Ma, S., Li, X., Wang, Y.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2016},
  title = {Churn detection and prediction in automotive supply industry},
  abstract = {Companies have both large certified enterprises and small unauthorized service providers as their competitors in the automotive supply industry. As technology related industries undergo more intensive competition, churn detection and prediction become essential to be precautious about leaving customers. The literature for churn detection offers numerous statistical and intelligent methods. In this study, Artificial Neural Networks and Decision Trees are applied to detect the churn in and analyze the validity of these methods for the automotive supply industry. The problem involves both categorical and continuous numerical decision inputs which cannot simultaneously fed into Decision Trees. In this case, continuous inputs should be divided into binary categorical ones by splitting into various intervals which are called buckets. Particle Swarm Optimization algorithm is implemented for finding optimal buckets for the churn problem data. Results indicate that while both algorithms are promising, the bucket tuning for Decision Trees complicate the churn detection process © 2016 Polish Information Processing Society.},
  doi = {10.15439/2016F245},
  journal = {Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016},
  bibtexkey = {Karapinar20161349},
  author = {Karapinar, H.C., Altay, A., Kayakutlu, G.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2016},
  title = {Application of Computational Intelligence to Predict Churn and Non-Churn of Customers in Indian Telecommunication},
  abstract = {In the modern society, mobile communication became the leading medium of communication. Now the public policies and standardization of mobile communication allows customers to switch from one service provider to another service provider easily. One of the most critical challenges in data and voice telecommunication service industry is retaining customers. The cost of retaining an existing customer is lesser than the cost of getting a new customer. So service providers now shifted their focus from customer acquisition to customer retention. As a result, churn prediction has emerged as the most essential Business Intelligence (BI) application that aims to identify the customers who are about to transfer their service to a competitor i.e. To churn. In this paper, we proposed Counter Propagation Neural Networks (CPNN), Classification and Regression Trees (CART), J48 and fuzzy ARTMAP to predict customer churn and non-churn in telecommunication sector. The dataset analyzed is taken from Indian Telecommunication Service Industry. © 2015 IEEE.},
  doi = {10.1109/CICN.2015.123},
  journal = {Proceedings - 2015 International Conference on Computational Intelligence and Communication Networks, CICN 2015},
  bibtexkey = {Mohanty2016598},
  author = {Mohanty, R., Rani, K.J.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2016},
  title = {Domain adaptation in the absence of source domain data},
  abstract = {The overwhelming majority of existing domain adaptation methods makes an assumption of freely available source domain data. An equal access to both source and target data makes it possible to measure the discrepancy between their distributions and to build representations common to both target and source domains. In reality, such a simplifying assumption rarely holds, since source data are routinely a subject of legal and contractual constraints between data owners and data customers. When source domain data can not be accessed, decision making procedures are often available for adaptation nevertheless. These procedures are often presented in the form of classification, identification, ranking etc. rules trained on source data and made ready for a direct deployment and later reuse. In other cases, the owner of a source data is allowed to share a few representative examples such as class means. In this paper we address the domain adaptation problem in real world applications, where the reuse of source domain data is limited to classification rules or a few representative examples. We extend the recent techniques of feature corruption and their marginalization, both in supervised and unsupervised settings. We test and compare them on private and publicly available source datasets and show that significant performance gains can be achieved despite the absence of source data and shortage of labeled target data. © 2016 ACM.},
  doi = {10.1145/2939672.2939716},
  journal = {Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  bibtexkey = {Chidlovskii2016451},
  author = {Chidlovskii, B., Clinchant, S., Csurka, G.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {A Survey on Evolutionary Computation Approaches to Feature Selection},
  abstract = {Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research. © 1997-2012 IEEE.},
  doi = {10.1109/TEVC.2015.2504420},
  journal = {IEEE Transactions on Evolutionary Computation},
  bibtexkey = {Xue2016606},
  author = {Xue, B., Zhang, M., Browne, W.N., Yao, X.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Identifying (Quasi) Equally Informative Subsets in Feature Selection Problems for Classification: A Max-Relevance Min-Redundancy Approach},
  abstract = {An emerging trend in feature selection is the development of two-objective algorithms that analyze the tradeoff between the number of features and the classification performance of the model built with these features. Since these two objectives are conflicting, a typical result stands in a set of Pareto-efficient subsets, each having a different cardinality and a corresponding discriminating power. However, this approach overlooks the fact that, for a given cardinality, there can be several subsets with similar information content. The study reported here addresses this problem, and introduces a novel multiobjective feature selection approach conceived to identify: 1) a subset that maximizes the performance of a given classifier and 2) a set of subsets that are quasi equally informative, i.e., have almost same classification performance, to the performance maximizing subset. The approach consists of a wrapper [Wrapper for Quasi Equally Informative Subset Selection (W-QEISS)] built on the formulation of a four-objective optimization problem, which is aimed at maximizing the accuracy of a classifier, minimizing the number of features, and optimizing two entropy-based measures of relevance and redundancy. This allows conducting the search in a larger space, thus enabling the wrapper to generate a large number of Pareto-efficient solutions. The algorithm is compared against the mRMR algorithm, a two-objective wrapper and a computationally efficient filter [Filter for Quasi Equally Informative Subset Selection (F-QEISS)] on 24 University of California, Irvine, (UCI) datasets including both binary and multiclass classification. Experimental results show that W-QEISS has the capability of evolving a rich and diverse set of Pareto-efficient solutions, and that their availability helps in: 1) studying the tradeoff between multiple measures of classification performance and 2) understanding the relative importance of each feature. The quasi equally informative subsets are identified at the cost of a marginal increase in the computational time thanks to the adoption of Borg Multiobjective Evolutionary Algorithm and Extreme Learning Machine as global optimization and learning algorithms, respectively. © 2013 IEEE.},
  doi = {10.1109/TCYB.2015.2444435},
  journal = {IEEE Transactions on Cybernetics},
  bibtexkey = {Karakaya20161424},
  author = {Karakaya, G., Galelli, S., Ahipasaoglu, S.D., Taormina, R.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Finding influential users in social media using association rule learning},
  abstract = {Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods. © 2016 by the authors.},
  doi = {10.3390/e18050164},
  journal = {Entropy},
  bibtexkey = {Erlandsson2016},
  author = {Erlandsson, F., BrÃ³dka, P., Borg, A., Johnson, H.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2016},
  title = {Predicting churn of expert respondents in social networks using data mining techniques: A case study of stack overflow},
  abstract = {In Q&A social networks, the few respondents that answer most of the questions are an asset to that network. Being able to predict the churn of these expert respondents will enable the owners of such network put things in place in order to keep them. In this paper, we predicted the churn of expert respondents in Stack Overflow. We identified experts based on the InDegree of the respondents and the value of the incentives earned by these experts from the questions they have answered in the past. Using four data mining techniques: logistic regression, neural networks, support vector machines and random forests, we predicted user churn and evaluated our results with four evaluation metrics: percentage correctly classified, area under receiver operating characteristic curve, precision and recall. Of the four data mining algorithms, random forests performed best with PCC of 76%, ROC area of 0.82, precision of 0.76 and recall of 0.77. © 2015 IEEE.},
  doi = {10.1109/ICMLA.2015.120},
  journal = {Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015},
  bibtexkey = {Adaji2016182},
  author = {Adaji, I., Vassileva, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Social media optimization: Identifying an optimal strategy for increasing network size on Facebook},
  abstract = {This paper aims to create an expert system that yields an optimal strategy for increasing network size on Facebook. Data were obtained from 5488 Facebook users by means of a custom-built Facebook application. We computed a total of 426 variables. Using these data we estimated a predictive model of network size which is subsequently used in a prescriptive model. The former is estimated with Random Forest and the latter with a Genetic Algorithm. The results indicate that the proposed expert system can identify an optimal social media strategy. The system delivers concrete recommendations about, for example, the optimal time between status updates. The analysis reveals that network size can be increased by 61% if the optimal strategy is adopted. This study contributes to literature in the following two ways. First it devises a novel prescriptive social media expert system relying on an unprecedented variety of social media data. The results indicate that the system is effective and a viable strategic tool for increasing network size. Second it provides a list of the top drivers allowing future research to build similar systems efficiently. © 2015 Elsevier Ltd.},
  doi = {10.1016/j.omega.2015.04.017},
  journal = {Omega (United Kingdom)},
  bibtexkey = {Ballings201615},
  author = {Ballings, M., Van den Poel, D., Bogaert, M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {The added value of Facebook friends data in event attendance prediction},
  abstract = {This paper seeks to assess the added value of a Facebook user's friends data in event attendance prediction over and above user data. For this purpose we gathered data of users that have liked an anonymous European soccer team on Facebook. In addition we obtained data from all their friends. In order to assess the added value of friends data we have built two models for five different algorithms (Logistic Regression, Random Forest, Adaboost, Neural Networks and Naive Bayes). The baseline model contained only user data and the augmented model contained both user and friends data. We employed five times two-fold cross-validation and the Wilcoxon signed rank test to validate our findings. The results suggest that the inclusion of friends data in our predictive model increases the area under the receiver operating characteristic curve (AUC). Out of five algorithms, the increase is significant for three algorithms, marginally significant for one algorithm, and not significant for one algorithm. The increase in AUC ranged from 0.21%-points to 0.82%-points. The analyses show that a top predictor is the number of friends that are attending the focal event. To the best of our knowledge this is the first study that evaluates the added value of friends network data over and above user data in event attendance prediction on Facebook. These findings clearly indicate that including network data in event prediction models is a viable strategy for improving model performance. © 2015 Elsevier B.V. All rights reserved.},
  doi = {10.1016/j.dss.2015.11.003},
  journal = {Decision Support Systems},
  bibtexkey = {Bogaert201626},
  author = {Bogaert, M., Ballings, M., Van Den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Intangible assets evaluation: The machine learning perspective},
  abstract = {The lack of regulations and disclosures regarding intangible capital has made it rather difficult for investors and creditors to evaluate a firm<U+05F3>s intangible value before making the associated investment and loan decisions. This study represents an initial attempt to compare/contrast different types of machine learning techniques and identify the optimal prediction model for intangible assets. In addition, this paper shows that machine learning can be used effectively for the problem of intangible assets evaluation. To be specific, five classification algorithms are considered: decision trees (DT), artificial neural networks (ANN), naïve Bayes, support vector machines (SVM) and k-Nearest Neighbors (k-NN). Consequently, thirty prediction models are constructed for comparison, including five single classifiers, boosting and bagging based classifier ensembles, and the combination of k-means clustering, single classifiers and classifier ensembles. The experimental results show that prediction models combining k-means with boosting/bagging based classifier ensembles perform much better than the other methods in terms of prediction accuracy, ROC Curve, as well as Type I and II errors. In particular, while the best single classifier, k-NN provides 78.24% prediction accuracy, k-means+bagging based DT ensembles provide the best performance to predict intangible assets with a prediction accuracy of 91.60%, 96.40% of ROC Curve and 18.65% of Type I and 6.34% of II errors, respectively. © 2015 Elsevier B.V.},
  doi = {10.1016/j.neucom.2015.10.041},
  journal = {Neurocomputing},
  bibtexkey = {Tsai2016110},
  author = {Tsai, C.-F., Lu, Y.-H., Hung, Y.-C., Yen, D.C.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Comparing Churn Prediction Techniques and Assessing Their Performance: A Contingent Perspective},
  abstract = {Customer retention has become a focal priority. However, the process of implementing an effective retention campaign is complex and dependent on firms’ ability to accurately identify both at-risk customers and those worth retaining. Drawing on empirical and simulated data from two online retailers, we evaluate the performance of several parametric and nonparametric churn prediction techniques, in order to identify the optimal modeling approach, dependent on context. Results show that under most circumstances (i.e., varying sample sizes, purchase frequencies, and churn ratios), the boosting technique, a nonparametric method, delivers superior predictability. Furthermore, in cases/contexts where churn is more rare, logistic regression prevails. Finally, where the size of the customer base is very small, parametric probability models outperform other techniques. © 2015, © The Author(s) 2015.},
  doi = {10.1177/1094670515616376},
  journal = {Journal of Service Research},
  bibtexkey = {Tamaddoni2016123},
  author = {Tamaddoni, A., Stakhovych, S., Ewing, M.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2016},
  title = {Churn perdiction in the telecom business},
  abstract = {Telecommunication companies are acknowledging the existing connection between customer satisfaction and company revenues. Customer churn in telecom refers to a customer that ceases his relationship with a company. Churn prediction in telecom has recently gained substantial interest of stakeholders, who noticed that retaining a customer is substantially cheaper that gaining a new one. This research compares six approaches using different algorithms that identify the clients who are closer to abandon their telecom provider. Those algorithms are: KNN, Naive Bayes, C4.5, Random Forest, Ada Boost and ANN. The use of real data provided by We Do technologies extended the refinement time necessary, but ensured that the developed algorithm and model can be applied to real world situations. The models are evaluated according to three criteria: are under curve, sensitivity and specificity, with special weight to the first two criteria. The Random Forest algorithm proved to be the most adequate in all the test cases. © 2016 IEEE.},
  doi = {10.1109/ICDIM.2016.7829775},
  journal = {2016 11th International Conference on Digital Information Management, ICDIM 2016},
  bibtexkey = {Esteves2016254},
  author = {Esteves, G., Mendes-Moreira, J.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2016},
  title = {Predicting user participation in social media},
  abstract = {Online social networking services like Facebook provides a popular way for users to participate in different communication groups and discuss relevant topics with each other. While users tend to have an impact on each other, it is important to better understand and analyze users behavior in specific online groups. For social networking sites it is of interest to know if a topic will be interesting for users or not. Therefore, this study examines the prediction of user participation in online social networks discussions, in which we argue that it is possible to predict user participation in a public group using common machine learning techniques. We are predicting user participation based on association rules built with respect to user activeness of current posts. In total, we have crawled and extracted 2,443 active users interacting on 610 posts with over 14,117 comments on Facebook. The results show that the proposed approach has a high level of accuracy and the systematic study clearly depicts the possibility to predict user participation in social networking sites. © Springer International Publishing Switzerland 2016.},
  doi = {10.1007/978-3-319-28361-6_10},
  journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  bibtexkey = {Erlandsson2016126},
  author = {Erlandsson, F., Borg, A., Johnson, H., BrÃ³dka, P.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Regaining drifting mobile communication customers: Predicting the odds of success of winback efforts with competing risks regression},
  abstract = {Mobile network operators (MNOs) make considerable efforts to reduce customer defection (= churn) by trying to motivate customers who announced to cancel their contract at the next legally possible date to withdraw their notification and to sign another postpaid contract or at least to accept a prepaid offer in case that they are unwilling to completely revoke their cancellation. Nevertheless, empirical evidence on factors significantly associated with the odds of success of an MNO's reactive winback attempt is scarce. As a consequence, this study explores the capability of socio-demographic, contract and service usage characteristics of MNO subscribers as well as their stated primary reason for contract termination to predict the likelihood of a fully successful winback at the individual customer level. In a sample of 305,466 postpaid residential customers of the German subsidiary of a multinational MNO, competing risks regression analysis suggests that younger customers with above average service usage levels who were already in a tariff plan bundling mobile voice and Internet access services, yet had received a subsidized device from their current provider in the past, had not originally signed their contract in the firm's own outlets and stated they cancelled their contract as a precautionary move or due to tariff level/structure reasons exhibit the highest prospects of full restoration. Moreover, the analysis reveals that the covariates studied and the competing risks regression technique achieve a satisfactory performance in predicting the outcome of the MNO's customer winback efforts. Results are discussed in terms of basic entry points of MNOs for improving both their reactive winback as well as their new customer acquisition strategies. ©2015 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2015.05.011},
  journal = {Expert Systems with Applications},
  bibtexkey = {Gerpott20157917},
  author = {Gerpott, T.J., Ahmadi, N.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Evaluating multiple classifiers for stock price direction prediction},
  abstract = {Stock price direction prediction is an important issue in the financial world. Even small improvements in predictive performance can be very profitable. The purpose of this paper is to benchmark ensemble methods (Random Forest, AdaBoost and Kernel Factory) against single classifier models (Neural Networks, Logistic Regression, Support Vector Machines and K-Nearest Neighbor). We gathered data from 5767 publicly listed European companies and used the area under the receiver operating characteristic curve (AUC) as a performance measure. Our predictions are one year ahead. The results indicate that Random Forest is the top algorithm followed by Support Vector Machines, Kernel Factory, AdaBoost, Neural Networks, K-Nearest Neighbors and Logistic Regression. This study contributes to literature in that it is, to the best of our knowledge, the first to make such an extensive benchmark. The results clearly suggest that novel studies in the domain of stock price direction prediction should include ensembles in their sets of algorithms. Our extensive literature review evidently indicates that this is currently not the case. © 2015 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2015.05.013},
  journal = {Expert Systems with Applications},
  bibtexkey = {Ballings20157046},
  author = {Ballings, M., Van Den Poel, D., Hespeels, N., Gryp, R.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Using metaheuristics to optimize the combination of classifier and cluster ensembles},
  abstract = {We investigate how to make a simpler version of an existing algorithm, named C3E, from Consensus between Classification and Clustering Ensembles, more user-friendly by automatically tuning its main parameters with the use of metaheuristics. In particular, C3E based on a Squared Loss function, C3E-SL, assumes an optimization procedure that takes as input class membership estimates from existing classifiers, as well as a similarity matrix from a cluster ensemble operating solely on the new target data, to provide a consolidated classification of the target data. To do so, two parameters have to be defined a priori, namely: the relative importance of classifier and cluster ensembles and the number of iterations of the algorithm. In some practical applications, these parameters can be optimized via time consuming grid search approaches based on cross-validation procedures. This paper shows that seven metaheuristics for parameter optimization yield classifiers as accurate as those obtained from grid search, but taking half the running time. More precisely, and by assuming a trade-off between user-friendliness and accuracy, experiments performed on twenty real-world datasets suggest that CMA-ES, DE, and SaDE are the best alternatives to optimize the C3E-SL parameters. © 2015 - IOS Press and the author(s). All rights reserved.},
  doi = {10.3233/ICA-150485},
  journal = {Integrated Computer-Aided Engineering},
  bibtexkey = {Coletta2015229},
  author = {Coletta, L.F.S., Hruschka, E.R., Acharya, A., Ghosh, J.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2015},
  title = {CRM in social media: Predicting increases in Facebook usage frequency},
  abstract = {The purpose of this study is to (1) assess the feasibility of predicting increases in Facebook usage frequency, (2) evaluate which algorithms perform best, (3) and determine which predictors are most important. We benchmark the performance of Logistic Regression, Random Forest, Stochastic Adaptive Boosting, Kernel Factory, Neural Networks and Support Vector Machines using five times twofold cross-validation. The results indicate that it is feasible to create models with high predictive performance. The top performing algorithm was Stochastic Adaptive Boosting with a cross-validated AUC of 0.66 and accuracy of 0.74. The most important predictors include deviation from regular usage patterns, frequencies of likes of specific categories and group memberships, average photo album privacy settings, and recency of comments. Facebook and other social networks alike could use predictions of increases in usage frequency to customize its services such as pacing the rate of advertisements and friend recommendations, or adapting News Feed content altogether. The main contribution of this study is that it is the first to assess the prediction of increases in usage frequency in a social network. © 2015 Elsevier B.V. All rights reserved.},
  doi = {10.1016/j.ejor.2015.01.001},
  journal = {European Journal of Operational Research},
  bibtexkey = {Ballings2015248},
  author = {Ballings, M., Van Den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Feature-selection-based dynamic transfer ensemble model for customer churn prediction},
  abstract = {Customer churn prediction is one of the key steps to maximize the value of customers for an enterprise. It is difficult to get satisfactory prediction effect by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study proposes a feature-selection-based dynamic transfer ensemble (FSDTE) model that aims to introduce transfer learning theory for utilizing the customer data in both the target and related source domains. The model mainly conducts a two-layer feature selection. In the first layer, an initial feature subset is selected by GMDH-type neural network only in the target domain. In the second layer, several appropriate patterns from the source domain to target training set are selected, and some features with higher mutual information between them and the class variable are combined with the initial subset to construct a new feature subset. The selection in the second layer is repeated several times to generate a series of new feature subsets, and then, we train a base classifier in each one. Finally, a best base classifier is selected dynamically for each test pattern. The experimental results in two customer churn prediction datasets show that FSDTE can achieve better performance compared with the traditional churn prediction strategies, as well as three existing transfer learning strategies. © 2014, Springer-Verlag London.},
  doi = {10.1007/s10115-013-0722-y},
  journal = {Knowledge and Information Systems},
  bibtexkey = {Xiao201529},
  author = {Xiao, J., Xiao, Y., Huang, A., Liu, D., Wang, S.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector},
  abstract = {High-cardinality attributes are categorical attributes that contain a very large number of distinct values, like for example: family names, ZIP codes or bank account numbers. Within a predictive modeling setting, such features could be highly informative as it might be useful to know that people live in the same village or pay with the same bank account number. Despite this notable and intuitive advantage, high-cardinality attributes are rarely used in predictive modeling. The main reason for this is that including these attributes by using traditional transformation methods is either impossible due to anonymization of the data (when using semantic grouping of the values) or will vastly increase the dimensionality of the data set (when using dummy encoding), thereby making it difficult or even impossible for most classification techniques to build prediction models. The main contributions of this work are (1) the introduction of several possible transformation functions coming from different domains and contexts, that allow the inclusion of high-cardinality features in predictive models. (2) Using a unique data set of a large energy company with more than 1 million customers, we show that adding such features indeed improves the predictive performance of the model significantly. Moreover, (3) we empirically demonstrate that having more data leads to better prediction models, which is not observed for "traditional" data. As such, we also contribute to the area of big data analytics. © 2015 Elsevier B.V.All right sreserved.},
  doi = {10.1016/j.dss.2015.02.007},
  journal = {Decision Support Systems},
  bibtexkey = {Moeyersoms201572},
  author = {Moeyersoms, J., Martens, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean-variance customer portfolio: The case of bank marketing campaigns},
  abstract = {In this paper we propose a novel recurrent reinforcement learning approach for controllable Markov chains that adjusts its policies according to a preprocessing and an actor-critic architecture. The preprocessing is proposed when learning a new task is needed from reinforcement based on a priori knowledge, in order to decrease computation time and not explore and not learn everything from scratch. The actor-critic architecture is based on an iterated quadratic/Lagrange programming maximization algorithm for computing the optimal strategies of the mean-variance customer portfolio. This process can be viewed as a specific form of asynchronous value iteration with optimized computational properties. The use of only the value-maximizing action at each state is unlikely in practice. Then, a specific selection of policies is used to ensure convergence. The reinforcement model proposed predicts a learning process that takes the risk of the customer portfolio into account. The resulting policies dynamically optimize the customer portfolio. We propose to apply three different learning rules, based on the transition matrices, the utilities and the costs, to estimate the objective function for the current policies. In particular, the learning rule related to estimate the real costs imposes restrictions over the formulation of the portfolio: costs cannot be underestimated or overestimated. The learning rules allow the process to make use of past experiences and decide on future actions to take in or around a given state of the Markov chain. We provide implementation details of the learning process and the complete algorithm. In addition, we illustrate our approach with a bank marketing application example for showing the viability of the model for solving realistic problems. © 2015 Elsevier Ltd.},
  doi = {10.1016/j.engappai.2015.08.011},
  journal = {Engineering Applications of Artificial Intelligence},
  bibtexkey = {SÃ¡nchez201582},
  author = {SÃ¡nchez, E.M., Clempner, J.B., Poznyak, A.S.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Estimating Social Influences from Social Networking Sites-Articulated Friendships versus Communication Interactions},
  abstract = {Despite the ubiquity of social networking sites, the online social networking industry is in search of effective marketing strategies to better profit from their established user base. Social media marketing strategies build on the premise that the social network of online users can be predicted and social influences among online users can be estimated. However, the existence of various heterogeneous social interactions on social networking sites presents a challenge for social network prediction and social influence estimation. In this article we draw upon the literatures on self-presentation on social networking sites and signaling in online social networking to categorize six heterogeneous online social interactions on social networking sites into two types-articulated friendships and communication interactions. This article provides empirical evidence for the differences between articulated friendships and communication interactions and the corresponding articulated and communication networks. In order to compare the impacts of the social influences based on these two networks, we utilize support vector machines to build a classifier to predict virtual community membership and we further estimate the marginal effects of these social influences using a two-stage probit least squares method. We find significant explanatory power of social influences in predicting virtual community membership. Although the communication network is much sparser than the articulated network, social influences based on the communication network achieve similar performance as the articulated network. These findings provide important implications for social media marketing as well as the management of virtual communities. © 2015 Decision Sciences Institute.},
  doi = {10.1111/deci.12118},
  journal = {Decision Sciences},
  bibtexkey = {Guo2015135},
  author = {Guo, H., Pathak, P., Cheng, H.K.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Mining the dominant patterns of customer shifts between segments by using top-k and distinguishing sequential rules},
  abstract = {Purpose – The purpose of this paper is to detect different behavioral groups and the dominant patterns of customer shifts between segments of different values over time. Design/methodology/approach – A new hybrid methodology is presented based on clustering techniques and mining top-k and distinguishing sequential rules. This methodology is implemented on the data of 14,772 subscribers of a mobile phone operator in Tehran, the capital of Iran. The main data include the call detail records and event detail records data that was acquired from the IT department of this operator. Findings – Seven different behavioral groups of customer shifts were identified. These groups and the corresponding top-k rules represent the dominant patterns of customer behavior. The results also explain the relation of customer switching behavior and segment instability, which is an open problem. Practical implications – The findings can be helpful to improve marketing strategies and decision making and for prediction purposes. The obtained rules are relatively easy to interpret and use; this can strengthen the practicality of results. Originality/value – A new hybrid methodology is proposed that systematically extracts the dominant patterns of customer shifts. This paper also offers a new definition and framework for discovering distinguishing sequential rules. Comparing with Markov chain models, this study captures the customer switching behavior in different levels of value through interpretable sequential rules. This is the first study that uses sequential and distinguishing rules in this domain. © 2015, Emerald Group Publishing Limited.},
  doi = {10.1108/MD-09-2014-0551},
  journal = {Management Decision},
  bibtexkey = {Akhondzadeh-Noughabi20151976},
  author = {Akhondzadeh-Noughabi, E., Albadvi, A.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Performance Prediction and Sensitivity Analysis of SAW Gas Sensors},
  abstract = {An improved adaptive neuro-fuzzy inference system (IANFIS) is proposed to build a model to predict the resonant frequency shift performance of surface acoustic wave (SAW) gas sensors. In the proposed IANFIS, by directly minimizing the root-mean-squared-error performance criterion, the Taguchi-genetic learning algorithm is used in the ANFIS to find both the optimal premise and consequent parameters and to simultaneously determine the most suitable membership functions. The five design parameters of SAW gas sensors are considered to be the input variables of the IANFIS model. The input variables include the number of electrode finger pairs, the electrode overlap, the separation distance of two interdigital transducers on the substrate, the dimensions of the stable temperature-cut (ST-cut) quartz substrate, and the electrode thickness. The output variable of the IANFIS model is composed of the resonant frequency shift performance. The results predicted by the proposed IANFIS are compared with those obtained by the back-propagation neural network. The comparison has shown that the performance prediction of resonant frequency shift using the proposed IANFIS is effective. In addition, the sensitivity analyses of the five design parameters have also shown that both the electrode overlap and the dimensions of the ST-cut quartz substrate have the most influence on the resonant frequency shift performance. © 2013 IEEE.},
  doi = {10.1109/ACCESS.2015.2478789},
  journal = {IEEE Access},
  bibtexkey = {Tsai20151614},
  author = {Tsai, J.-T., Chiu, K.-Y., Chou, J.-H.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2014},
  title = {Dynamic churn prediction framework with more effective use of rare event data: The case of private banking},
  abstract = {Customer churn prediction literature has been limited to modeling churn in the next (feasible) time period. On the other hand, lead time specific churn predictions can help businesses to allocate retention efforts across time, as well as customers, and identify early triggers and indicators of customer churn. We propose a dynamic churn prediction framework for generating training data from customer records, and leverage it for predicting customer churn within multiple horizons using standard classifiers. Further, we empirically evaluate the proposed approach in a case study about private banking customers in a European bank. The proposed framework includes customer observations from different time periods, and thus addresses the absolute rarity issue that is relevant for the most valuable customer segment of many companies. It also increases the sampling density in the training data and allows the models to generalize across behaviors in different time periods while incorporating the impact of the environmental drivers. As a result, this framework significantly increases the prediction accuracy across prediction horizons compared to the standard approach of one observation per customer; even when the standard approach is modified with oversampling to balance the data, or lags of customer behavior features are added as additional predictors. The proposed approach to dynamic churn prediction involves a set of independently trained horizon-specific binary classifiers that use the proposed dataset generation framework. In the absence of predictive dynamic churn models, we had to benchmark survival analysis which is used predominantly as a descriptive tool. The proposed method outperforms survival analysis in terms of predictive accuracy for all lead times, with a much lower variability. Further, unlike Cox regression, it provides horizon specific ranking of customers in terms of churn probability which allows allocation of retention efforts across customers and time periods. © 2014 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2014.06.018},
  journal = {Expert Systems with Applications},
  bibtexkey = {GÃ¼rAli20147889},
  author = {GÃ¼r Ali, Ã–., AritÃ¼rk, U.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2014},
  title = {Comparative study of dimension reduction methods for highly imbalanced overlapping churn data},
  abstract = {Retention of possible churning customer is one of the most important issues in customer relationship management, so companies try to predict churn customers using their large-scale high-dimensional data. This study focuses on dealing with large data sets by reducing the dimensionality. By using six different dimension reduction methods-Principal Component Analysis (PCA), factor analysis (FA), locally linear embedding (LLE), local tangent space alignment (LTSA), locally preserving projections (LPP), and deep auto-encoder-our experiments apply each dimension reduction method to the training data, build a classification model using the mapped data and then measure the performance using hit rate to compare the dimension reduction methods. In the result, PCA shows good performance despite its simplicity, and the deep auto-encoder gives the best overall performance. These results can be explained by the characteristics of the churn prediction data that is highly correlated and overlapped over the classes. We also proposed a simple out-of-sample extension method for the nonlinear dimension reduction methods, LLE and LTSA, utilizing the characteristic of the data. © 2014 KIIE.},
  doi = {10.7232/iems.2014.13.4.454},
  journal = {Industrial Engineering and Management Systems},
  bibtexkey = {Lee2014454},
  author = {Lee, S., Koo, B., Jung, K.-H.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2014},
  title = {The importance of social embeddedness: Churn models at mobile providers},
  abstract = {This article argues the importance of social embeddedness at mobile providers by examining the effects of customers' network topological properties on churn probability-the probability of a customer switching from one telecommunication provider to another. This article uses data from regional snowball sampling-the only practically feasible network sampling method-to identify groups with significantly different churn ratios for customers with different network topological properties. Clear evidence indicates that individual network characteristics (node-level metrics) have considerable impact on churn probabilities. The inclusion of network-related measures in the churn model allows a longer-term projection of churners and improves the predictive power of the model. With no possibility to carry out repeated sampling, sample stability was checked through simulation results. On the one hand, this article highlights the importance and effectiveness of the provider's tailored marketing campaigns by showing that customers targeted by direct marketing campaigns are less threatened by churn than nontargeted customers. On the other, this article shows that social embeddedness blocks the impact of the very same marketing efforts. This article forwards the idea that social embeddedness, also prevalent in vendor switching, can be extended to understanding the development of professional societies threatened by membership churn. © 2014 Decision Sciences Institute.},
  doi = {10.1111/deci.12057},
  journal = {Decision Sciences},
  bibtexkey = {Benedek2014175},
  author = {Benedek, G., LublÃ³y, A., Vastag, G.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2014},

repository: pesobreiro/SLRDropout

repo_name: pesobreiro/SLRDropout

extension: bib