An application of support vector machines for customer churn analysis: Credit card case

Sun Kim, Kyung Shik Shin, Kyungdo Park

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations

Abstract

This study investigates the effectiveness of support vector machines (SVM) approach in detecting the underlying data pattern for the credit card customer churn analysis. This article introduces a relatively new machine learning technique, SVM, to the customer churning problem in attempt to provide a model with better prediction accuracy. To compare the performance of the proposed model, we used a widely adopted and applied Artificial Intelligence (AI) method, back-propagation neural networks (BPN) as a benchmark. The results demonstrate that SVM outperforms BPN. We also examine the effect of the variability in performance with respect to various values of parameters in SVM.

Original languageEnglish
Pages (from-to)636-647
Number of pages12
JournalLecture Notes in Computer Science
Volume3611
Issue numberPART II
DOIs
StatePublished - 2005
EventFirst International Conference on Natural Computation, ICNC 2005 - Changsha, China
Duration: 27 Aug 200529 Aug 2005

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