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

Title
An application of support vector machines for customer churn analysis: Credit card case
Authors
Kim S.Shin K.-S.Park K.
Ewha Authors
신경식박경도
SCOPUS Author ID
신경식scopus
Issue Date
2005
Journal Title
Lecture Notes in Computer Science
ISSN
0302-9743JCR Link
Citation
Lecture Notes in Computer Science vol. 3611, no. PART II, pp. 636 - 647
Indexed
SCOPUS scopus
Document Type
Conference Paper
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.
Appears in Collections:
경영대학 > 경영학전공 > Journal papers
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