Title
Rule extraction from support vector machine using modified active learning based approach: an application to CRM
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.
Year
DOI
Venue
2010
10.1007/978-3-642-15387-7_50
KES (1)
Keywords
Field
DocType
support vector machine,empirical result,proposed rule extraction approach,rule extraction,business intelligence cup,business perspective,bank credit card,black box model,best sensitivity,new approach,churned customer,business intelligence,support vector machines,active learning
Black box (phreaking),Customer relationship management,Active learning,Active learning (machine learning),Electronic performance support systems,Computer science,Support vector machine,Artificial intelligence,Business intelligence,Machine learning
Conference
Volume
ISSN
ISBN
6276
0302-9743
3-642-15386-0
Citations 
PageRank 
References 
3
0.48
29
Authors
3
Name
Order
Citations
PageRank
M. A. H. Farquad1803.84
V. Ravi244325.29
Raju S. Bapi322630.87