Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 신경식 | * |
dc.contributor.author | 박경도 | * |
dc.date.accessioned | 2018-08-17T16:30:09Z | - |
dc.date.available | 2018-08-17T16:30:09Z | - |
dc.date.issued | 2005 | * |
dc.identifier.issn | 0302-9743 | * |
dc.identifier.other | OAK-17560 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/245365 | - |
dc.description.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. © Springer-Verlag Berlin Heidelberg 2005. | * |
dc.description.sponsorship | Ziangtan University | * |
dc.language | English | * |
dc.title | An application of support vector machines for customer churn analysis: Credit card case | * |
dc.type | Conference Paper | * |
dc.relation.issue | PART II | * |
dc.relation.volume | 3611 | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 636 | * |
dc.relation.lastpage | 647 | * |
dc.relation.journaltitle | Lecture Notes in Computer Science | * |
dc.identifier.scopusid | 2-s2.0-26844516299 | * |
dc.author.google | Kim S. | * |
dc.author.google | Shin K.-S. | * |
dc.author.google | Park K. | * |
dc.contributor.scopusid | 신경식(56927436200) | * |
dc.date.modifydate | 20240118131805 | * |