Abstract
Customer Churn Prediction is an increasingly pressing issue in today’s ever-competitive commercial arena. Although there are several researches in churn prediction, but the accuracy rate, which is very important to business, is not high enough. Recently, Support Vector Machines (SVMs), based on statistical learning theory, are gaining applications in the areas of data mining, machine learning, computer vision and pattern recognition because of high accuracy and good generalization capability. But there has no report about using SVM to Customer Churn Prediction. According to churn data set characteristic, the number of negative examples is very small, we introduce an improved one-class SVM. And we have tested our method on the wireless industry customer churn data set. Our method has been shown to perform very well compared with other traditional methods, ANN, Decision Tree, and Naïve Bays.
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References
Chiang, D.-A., Wang, Y.-F., Lee, S.-L., Lin, C.-J.: Goal-oriented sequential pattern for network banking churn. Expert Systems with Applications 25, 293–302 (2003)
Teradata, D.: Teradata Center for Customer Relationship Management. Retrieved on: November 7 (2002)
Scholkopf, B., et al.: Estimating the support of a High-Dimensional Distribution, Technical Report, Department of Computer Science, University of Haifa, Haifa (2001)
Neslin, S.A., Gupta, S., Kamakura, W., Lu, J., Mason, C.: Defection Detection: Improving Predictive Accuracy of Customer Churn Models
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Trafalis, T.B.: Support vector machine for regression and applications to financial forecasting. In: Proceedings of the International Joint Conference on Neural Networks, vol. 6, pp. 348–353 (2000)
Scholkopf, B., Platt, J.C., Shawe, J.T., Smola, A.J., Williamson, R.C.: Estimation the support of a high-dimensional Distribution, Technical Report MSR-TR-99-87, Microsoft Research
Li, K., Huang, H., Tian, S., Xu, W.: Improving one-class SVM for Anomaly detection. In: Proceedings of the second international conference on machine learning and cybernetics, Xi’an, November 2-5 (2003)
Nath, S.V., Behara, R.S.: Customer churn analysis in the wireless industry A data mining approach. In: Proceedings - Annual Meeting of the Decision Sciences Institute, pp. 505–510 (2003)
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhao, Y., Li, B., Li, X., Liu, W., Ren, S. (2005). Customer Churn Prediction Using Improved One-Class Support Vector Machine. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_36
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DOI: https://doi.org/10.1007/11527503_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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