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dc.contributor.author신경식-
dc.contributor.author박경도-
dc.date.accessioned2018-08-17T16:30:09Z-
dc.date.available2018-08-17T16:30:09Z-
dc.date.issued2005-
dc.identifier.issn0302-9743-
dc.identifier.otherOAK-17560-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/245365-
dc.description.abstractThis 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. © Springer-Verlag Berlin Heidelberg 2005.-
dc.description.sponsorshipZiangtan University-
dc.languageEnglish-
dc.titleAn application of support vector machines for customer churn analysis: Credit card case-
dc.typeConference Paper-
dc.relation.issuePART II-
dc.relation.volume3611-
dc.relation.indexSCOPUS-
dc.relation.startpage636-
dc.relation.lastpage647-
dc.relation.journaltitleLecture Notes in Computer Science-
dc.identifier.scopusid2-s2.0-26844516299-
dc.author.googleKim S.-
dc.author.googleShin K.-S.-
dc.author.googlePark K.-
dc.contributor.scopusid신경식(21735905700)-
dc.date.modifydate20180817095619-
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경영대학 > 경영학전공 > Journal papers
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