Commenced in January 2007
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Cost Sensitive Feature Selection in Decision-Theoretic Rough Set Models for Customer Churn Prediction: The Case of Telecommunication Sector Customers

Authors: Emel Kızılkaya Aydogan, Mihrimah Ozmen, Yılmaz Delice


In recent days, there is a change and the ongoing development of the telecommunications sector in the global market. In this sector, churn analysis techniques are commonly used for analysing why some customers terminate their service subscriptions prematurely. In addition, customer churn is utmost significant in this sector since it causes to important business loss. Many companies make various researches in order to prevent losses while increasing customer loyalty. Although a large quantity of accumulated data is available in this sector, their usefulness is limited by data quality and relevance. In this paper, a cost-sensitive feature selection framework is developed aiming to obtain the feature reducts to predict customer churn. The framework is a cost based optional pre-processing stage to remove redundant features for churn management. In addition, this cost-based feature selection algorithm is applied in a telecommunication company in Turkey and the results obtained with this algorithm.

Keywords: Churn prediction, data mining, decision-theoretic rough set, feature selection.

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[1] Berson, A., & Smith, S. J. (2002). Building data mining applications for CRM. McGraw-Hill, Inc.
[2] Mattersion, R. (2001). Telecom churn management. Fuquay-Varina, NC: APDG Publishing.
[3] Au, W., Chan, C., & Yao, X. (2003). A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Transactions on Evolutionary Computation, 7, 532–545.
[4] Coussement, K., & den Poe, D. V. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameter selection techniques. Expert Systems with Applications, 34, 313–327.
[5] Lu, J. (2002). Predicting customer churn in the telecommunications industry––An application of survival analysis modeling using SAS. SAS User Group International (SUGI27) Online Proceedings, 114-27.
[6] John, H., Ashutosh, T., Rajkumar, R., Dymitr, R. (2007). Computer assisted customer churn management: State-of-the-art and future trends.
[7] Wei, C., & Chiu, I. (2002). Turning telecommunications call details to churn prediction: A data mining approach. Expert Systems with Applications, 23, 103–112.
[8] Kim, M. K., Park, M. C., & Jeong, D. H. (2004). The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommunications policy, 28(2), 145-159.
[9] Huang, B., Kechadi, M. T., & Buckley, B. (2012). Customer churn prediction in telecommunications. Expert Systems with Applications, 39(1), 1414-1425.
[10] Huang, B., Buckley, B., & Kechadi, T. M. (2010). Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications. Expert Systems with Applications, 37(5), 3638-3646.
[11] Q.H. Hu, H. Zhao, Z.X. Xie, D.R. Yu, Consistency based attribute reduction, in: Proceedings of PAKDD2007, LNAI, vol. 4426, Springer-Verlag, Berlin, Heidelberg, 2007, pp. 96–107.
[12] X.Y. Jia, K. Zheng, W.W. Li, T.T. Liu, L. Shang, Three-way decisions solution to filter spam email: an empirical study, in: Proceedings of RSCTC2012, LNAI, vol. 7413, 2012, pp. 287–296.
[13] H.X. Li, X.Z. Zhou, Risk decision making based on decision-theoretic rough set: a three-way view decision model, International Journal of Computational Intelligence Systems 4 (1) (2011) 1–11.
[14] D. Liu, H.X. Li, X.Z. Zhou, Two decades’ research on decision-theoretic rough sets, in: Proceedings of ICCI, 2010, pp. 968–973.
[15] Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, MA, 1991.
[16] Y.Y. Yao, Probabilistic approach to rough sets, Expert Systems 20 (2003) 287–297.
[17] Y.Y. Yao, Probabilistic rough set approximations, International Journal of Approximate Reasoning 49 (2008) 255–271.
[18] Y.Y. Yao, S.K.M. Wong, A decision theoretic framework for approximating concepts, International Journal of Man–Machine Studies 37 (6) (1992) 793–809.
[19] Y.Y. Yao, S.K.M. Wong, P. Lingras, A decision-theoretic rough set model, in: Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems, 1990, pp. 17–25.
[20] Y.Y. Yao, Y. Zhao, Attribute reductions in decision-theoretic rough set models, Information Sciences 178 (2008) 3356–3373.
[21] Jia, X., Liao, W., Tang, Z., & Shang, L. (2013). Minimum cost attribute reduction in decision-theoretic rough set models. Information Sciences, 219, 151-167.
[22] Wang, X., Yang, J., Teng, X., Xia, W., & Jensen, R. (2007). Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters, 28(4), 459-471.
[23] Wang, X., Yang, J., Jensen, R., & Liu, X. (2006). Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Computer methods and programs in biomedicine, 83(2), 147-156.
[24] Derrac, J., Cornelis, C., García, S., & Herrera, F. (2012). Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection. Information Sciences, 186(1), 73-92.
[25] Sun, L., Xu, J., & Tian, Y. (2012). Feature selection using rough entropy-based uncertainty measures in incomplete decision systems. Knowledge-Based Systems, 36, 206-216.
[26] Chebrolu, S., & Sanjeevi, S. G. (2015). Attribute Reduction in Decision-Theoretic Rough Set Model using Particle Swarm Optimization with the Threshold Parameters Determined using LMS Training Rule. Procedia Computer Science, 57, 527-536.
[27] Min, F., Hu, Q., & Zhu, W. (2014). Feature selection with test cost constraint. International Journal of Approximate Reasoning, 55(1), 167-179.
[28] Aydogan E., (2012) hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems, Appl. Soft Comput., vol. 12, no. 2, pp. 800806.
[29] Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., & Abbasi, U. (2014). Improved churn prediction in telecommunication industry using data mining techniques. Applied Soft Computing, 24, 994-1012.
[30] Ahn, J. H., Han, S. P., & Lee, Y. S. (2006). Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommunications policy, 30 (10), 552-568.
[31] Hung, S. Y., Yen, D. C., & Wang, H. Y. (2006). Applying data mining to telecom churn management. Expert Systems with Applications, 31(3), 515-524.
[32] Kim, N., Jung, K. H., Kim, Y. S., & Lee, J. (2012). Uniformly subsampled ensemble (USE) for churn management: Theory and implementation. Expert Systems with Applications, 39(15), 11839-11845.
[33] Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1-9.
[34] Huang, B., Buckley, B., & Kechadi, T. M. (2010). Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications. Expert Systems with Applications, 37(5), 3638-3646.
[35] Zhang, Y., Qi, J., Shu, H., & Li, Y. (2006, October). Case study on crm: Detecting likely churners with limited information of fixed-line subscriber. In Service Systems and Service Management, 2006 International Conference on (Vol. 2, pp. 1495-1500). IEEE.