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

doi:10.1007/s11704-010-0508-2

Front Comput Sci Chin

2010, Vol. 4

Issue (2) : 196-203 https://doi.org/10.1007/s11704-010-0508-2

RESEARCH ARTICLE

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

Lean YU^1,2(

), Shouyang WANG¹, Kin Keung LAI³

1. Institute of Systems Science, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190, China; 2. Hangzhou Key Laboratory of E-Business and Information Security, Hangzhou Normal University, Hangzhou 310036, China; 3. Department of Management Sciences, City University of Hong Kong, Hong Kong, China

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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)

Corresponding Author(s): YU Lean,Email:yulean@amss.ac.cn

Issue Date: 05 June 2010

Cite this article:

Lean YU,Shouyang WANG,Kin Keung LAI. Developing an SVM-based ensemble learning system for customer risk identification collaborating with customer relationship management[J]. Front Comput Sci Chin, 2010, 4(2): 196-203.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-010-0508-2
https://academic.hep.com.cn/fcs/EN/Y2010/V4/I2/196

Fig.1 General procedure of SVM-based ensemble learning system

Tab.1 The diversity source of ensemble members of the eight SVM-based ensemble models

Tab.2 Total accuracy for different ensemble models

Fig.2 Total accuracy of SVM-based ensemble models

Fig.3 Total accuracy of , , LogR ensemble and the ANN ensemble

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