Developing an SVM-based ensemble learning system for customer risk identification collaborating with customer relationship management

Abstract

In this study, we propose a support vector machine (SVM)-based ensemble learning system for customer relationship management (CRM) to help enterprise managers effectively manage customer risks from the risk aversion perspective. This system differs from the classical CRM for retaining and targeting profitable customers; the main focus of the proposed SVM-based ensemble learning system is to identify high-risk customers in CRM for avoiding possible loss. To build an effective SVM-based ensemble learning system, the effects of ensemble members’ diversity, ensemble member selection and different ensemble strategies on the performance of the proposed SVM-based ensemble learning system are each investigated in a practical CRM case. Through experimental analysis, we find that the Bayesian-based SVM ensemble learning system with diverse components and choose from space selection strategy show the best performance over various testing samples.

Keywords

support vector machines (SVM) / ensemble learning / diversity strategy / selection strategy / ensemble strategy / customer relationship management (CRM)

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant No. 90924024); the Knowledge Innovation Program of the Chinese Academy of Sciences and the Open Project of Hangzhou Key Laboratory of E-Business and Information Security, Hangzhou Normal University. The authors gratefully acknowledge the support of K. C. Wong Education Foundation, Hong Kong, China.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg