Rule extraction from support vector machine using modified active learning based approach: an application to CRM

Authors:
M. A. H. Farquad;V. Ravi;S. Bapi Raju
Affiliations:
Institute for Development and Research in banking Technology, Hyderabad, AP, India and Department of Computer & Information Sciences, University of Hyderabad, Hyderabad, AP, India;Institute for Development and Research in banking Technology, Hyderabad, AP, India;Department of Computer & Information Sciences, University of Hyderabad, Hyderabad, AP, India
Venue:
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Year:
2010

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Abstract

Despite superior generalization performance Support vector machines (SVMs) generate black box models. The process of converting such opaque models into transparent model is often regarded as rule extraction. This paper presents a new approach for rule extraction from SVMs using modified active learning based approach (mALBA), to predict churn in bank credit cards. The dataset is obtained from Business Intelligence Cup 2004, which is highly unbalanced with 93% loyal and 7% churned customers' data. Since identifying churner is paramount from business perspective, therefore considering sensitivity alone, the empirical results suggest that the proposed rule extraction approach using mALBA yielded the best sensitivity compared to other classifiers.