Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.

[1]  Zhi-Hua Zhou,et al.  Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .

[2]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Tiago Ferra de Sousa,et al.  Particle Swarm based Data Mining Algorithms for classification tasks , 2004, Parallel Comput..

[4]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[5]  Chih-Fong Tsai,et al.  Customer churn prediction by hybrid neural networks , 2009, Expert Syst. Appl..

[6]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[7]  Bart Baesens,et al.  Profit optimizing customer churn prediction with Bayesian network classifiers , 2014, Intell. Data Anal..

[8]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[9]  Ali Mustafa Qamar,et al.  Telecommunication subscribers' churn prediction model using machine learning , 2013, Eighth International Conference on Digital Information Management (ICDIM 2013).

[10]  Asifullah Khan,et al.  Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies , 2012, Comput. Electr. Eng..

[11]  Peter Tiño,et al.  Managing Diversity in Regression Ensembles , 2005, J. Mach. Learn. Res..

[12]  Sven Gotovac,et al.  Modeling Data Mining Applications for Prediction of Prepaid Churn in Telecommunication Services , 2010 .

[13]  Hossam Faris,et al.  Neighborhood Cleaning Rules and Particle Swarm Optimization for Predicting Customer Churn Behavior in Telecom Industry , 2014 .

[14]  Prabin Kumar Panigrahi,et al.  A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services , 2011, ArXiv.

[15]  Guo-en Xia,et al.  Model of Customer Churn Prediction on Support Vector Machine , 2008 .

[16]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[17]  Asifullah Khan,et al.  Genetic Programming and Adaboosting based churn prediction for Telecom , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[18]  Bogdan Gabrys,et al.  A Non-sequential Representation of Sequential Data for Churn Prediction , 2009, KES.

[19]  Li Xiu,et al.  Application of data mining techniques in customer relationship management: A literature review and classification , 2009, Expert Syst. Appl..

[20]  Ning Wang,et al.  Credit card customer churn prediction based on the RST and LS-SVM , 2009, 2009 6th International Conference on Service Systems and Service Management.

[21]  A. Keramati,et al.  Churn analysis for an Iranian mobile operator , 2011 .

[22]  Oleksandr Makeyev,et al.  Neural network with ensembles , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[23]  Xin Yao,et al.  Diversity exploration and negative correlation learning on imbalanced data sets , 2009, 2009 International Joint Conference on Neural Networks.

[24]  Xin Yao,et al.  Ensemble learning via negative correlation , 1999, Neural Networks.

[25]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[26]  David C. Yen,et al.  Applying data mining to telecom churn management , 2006, Expert Syst. Appl..

[27]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[28]  M. Tahar Kechadi,et al.  Customer churn prediction in telecommunications , 2012, Expert Syst. Appl..

[29]  Dirk Van den Poel,et al.  Handling class imbalance in customer churn prediction , 2009, Expert Syst. Appl..

[30]  Ashutosh Tiwari,et al.  Computer assisted customer churn management: State-of-the-art and future trends , 2007, Comput. Oper. Res..

[31]  Jorma Laurikkala,et al.  Improving Identification of Difficult Small Classes by Balancing Class Distribution , 2001, AIME.

[32]  Z. Ibrahim,et al.  Mobile phone customers churn prediction using elman and Jordan Recurrent Neural Network , 2012, 2012 7th International Conference on Computing and Convergence Technology (ICCCT).