Abstract
In customer churn prediction, an important yet challenging problem is the class imbalance of data distribution. After analyzing the disadvantages of the commonly used “two-step” methods, this study combines multiple classifiers ensemble technique, self-organizing data mining with cost-sensitive learning, and proposes one-step classifier ensemble model for imbalance data (OCEMI). For each test customer, it can adaptively select out the more appropriate one from the two kinds of dynamic ensemble approach: dynamic classifier selection (DCS) and dynamic ensemble selection (DES). Meanwhile, new cost-sensitive selection criteria for DCS and DES are constructed respectively to improve the classification ability for imbalanced data. The empirical results show that this strategy can be used to predict customer churn more effectively.
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References
Bhattacharya CB (1998) When customers are members: customer retention in paid membership contexts. J Acad Mark Sci 26(1):31–44
Burez J, Van den Poel D (2009) Handling class imbalance in customer churn prediction. Expert Syst Appl 36(3):4626–4636
Kim SY, Jung TS et al (2006) Customer segmentation and strategy development based on customer lifetime value: a case study. Expert Syst Appl 31(1):101–107
Verbeke W, Martens D et al (2011) Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst Appl 38(3):2354–2364
Crone SF, Lessmann S, Stahlbock R (2006) The impact of preprocessing on data mining: an evaluation of classifier sensitivity in direct marketing. Eur J Oper Res 173(3):781–800
Van Hulse J, Khoshgoftaar T (2009) Knowledge discovery from imbalanced and noisy data. Data Knowl Eng 68(12):1513–1542
Lemmens A, Croux C (2006) Bagging and boosting classification trees to predict churn. J Mark Res 43:276–286
Ivakhnenko AG (1976) The group method of data handling in prediction problems. Sov Autom Control 9(6):21–30
Xiao J, He C, Jiang X (2009) Structure identification of bayesian classifiers based on gmdh. Knowl -Based Syst 22(6):461–470
Xiao J, He C et al (2010) A dynamic classifier ensemble selection approach for noise data. Inf Sci 180(18):3402–3421
Ko AH, Sabourin R, Britto AS Jr (2008) From dynamic classifier selection to dynamic ensemble selection. Pattern Recognit 41(5):1718–1731
Woods K, Kegelmeyer WP Jr, Bowyer K (1997) Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal Mach Intell 19(4):405–410
Xiao J, He C (2009) Dynamic classifier ensemble selection based on gmdh. computational sciences and optimization, 2009. cso 2009. IEEE Int Joint Conf 1:731–734
Zhou ZH, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell 137(1):239–263
El-Melegy MT, Ahmed SM (2007) Neural networks in multiple classifier systems for remote-sensing image classification In Soft Computing in Image Processing. Springer, Berlin
Zou P, Hao YY, Li YJ (2010) Customer value segmentation based on cost-sensitive learning support vector machine. Int J Serv Technol Manage 14(1):126–137
Coussement K, Van den Poel D (2008) Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert Syst Appl 34(1):313–327
Acknowledgments
This research is partly supported by the Natural Science Foundation of China under Grant Nos. 71101100, 71273036 and 71211130018, New Teachers’ Fund for Doctor Stations, MOE (Ministry of Education) under Grant No. 20110181120047, Excellent Youth fund of Sichuan University under Grant No. 2013SCU04A08, China Postdoctoral Science Foundation under Grant Nos. 2011M500418 and 2012T50148, Frontier and Cross-innovation Foundation of Sichuan University under Grant No. skqy201352, Soft Science Foundation of Sichuan Province under Grant No. 2013ZR0016, Youth Project of Humanities and Social Sciences, MOE under Grant No. 13YJC630249, Scientific Research Starting Foundation for Young Teachers of Sichuan University under Grant No. 2012SCU11013.
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Xiao, J., Teng, G., He, C., Zhu, B. (2014). One-Step Classifier Ensemble Model for Customer Churn Prediction with Imbalanced Class. In: Xu, J., Cruz-Machado, V., Lev, B., Nickel, S. (eds) Proceedings of the Eighth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55122-2_72
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DOI: https://doi.org/10.1007/978-3-642-55122-2_72
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