Abstract
Customer churn prediction is one of the most important elements of a company’s Customer Relationship Management (CRM) strategy. In this study, two strategies are investigated to increase the lift performance of ensemble classification models, i.e. (i) using probability estimation trees (PETs) instead of standard decision trees as base classifiers, and (ii) implementing alternative fusion rules based on lift weights for the combination of ensemble member’s outputs. Experiments are conducted for four popular ensemble strategies on five real-life churn data sets. In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion rules. However, the effect varies for the different ensemble strategies. In particular, the results indicate an increase of lift performance of (i) Bagging by implementing C4.4 base classifiers, (ii) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (iii) AdaBoost by implementing both.
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References
Reinartz, W., Kumar, V.: The mismanagement of customer loyalty. Harvard Bus. Rev. 80, 86–94 (2002)
Shaw, M.J., Subramaniam, C., Tan, G.W., Welge, M.E.: Knowledge management and data mining for marketing. Decis. Support Syst. 31, 127–137 (2001)
Kim, Y.S.: Toward a successful CRM: variable selection, sampling, and ensemble. Decis. Support Syst. 41, 542–553 (2006)
Larivière, B., Van den Poel, D.: Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst. Appl. 29, 472–484 (2005)
Jinbo, S., Xiu, L., Wenhuang, L.: The application of AdaBoost in customer churn prediction. In: Proceedings of 2007 International Conference on Service Systems and Service Management (ICSSSM 2007), pp. 513–518 (2007)
Glady, N., Baesens, B., Croux, C.: Modeling churn using customer lifetime value. Eur. J. Oper. Res. 197, 402–411 (2009)
Lemmens, A., Croux, C.: Bagging and boosting classification trees to predict churn. J. Marketing Res. 43, 276–286 (2006)
Burez, J., Van den Poel, D.: Handling class imbalance in customer churn prediction. Expert Syst. Appl. 36, 4626–4636 (2009)
Provost, F., Domingos, P.: Tree induction for probability-based ranking. Mach. Learn. 52, 199–215 (2003)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kauffman Publishers, San Mateo (1993)
Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE T. Pattern Anal. 20, 832–844 (1998)
Panov, P., Dzeroski, S.: Combining bagging and random subspaces to create better ensembles. In: Berthold, M.R., ShaweTaylor, J., Lavrac, N. (eds.) IDA 2007. LNCS, vol. 4723, pp. 86–94. Springer, Heidelberg (2007)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)
Clemençon, S., Vayatis, N.: Tree-Based Ranking Methods. IEEE T. Inform. Theory 55, 4316–4336 (2009)
Kuncheva, L.I.: Combining pattern classifiers: methods and algorithms. John Wiley & Sons, Hoboken (2004)
Provost, F., Fawcett, T., Kohavi, R.: The Case against Accuracy Estimation for Comparing Induction Algorithms. In: Shavlik, J. (ed.) 15th International Conference on Machine Learning (ICML-1998), pp. 445–453. Morgan Kaufman, San Francisco (2000)
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach. Learn. 36, 105–139 (1999)
Rodríguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: A new classifier ensemble method. IEEE T. Pattern Anal. 28, 1619–1630 (2006)
Bryll, R., Gutierrez-Osuna, R., Quek, F.: Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recogn. 36, 1291–1302 (2003)
Prinzie, A., Van den Poel, D.: Constrained optimization of data-mining problems to improve model performance: A direct-marketing application. Expert Syst. Appl. 29, 630–640 (2005)
Cieslak, D., Chawla, N.: Analyzing PETs on imbalanced datasets when training and testing class distributions differ. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 519–526. Springer, Heidelberg (2008)
Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 1 (2009)
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De Bock, K.W., Van den Poel, D. (2010). Ensembles of Probability Estimation Trees for Customer Churn Prediction. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_7
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