Between Data Science and Applied Data Analysis pp 604-612 | Cite as
Support Vector Machines for Credit Scoring: Comparing to and Combining With Some Traditional Classification Methods
Abstract
Credit scoring is being used in order to assign credit applicants to good and bad risk classes. This paper investigates the credit scoring performance of a nonstandard neural network technique: support vector machines (SVM). Using empirical data, the results of the SVM are compared with more traditional methods including linear discriminant analysis and logistic regression. Furthermore, a two-step approach is being tested: first SVM selects the most informative cases, and subsequently, these are used as inputs to linear discriminant analysis and logistic regression. Extensive experiments show that SVM outperforms the more traditional, computationally less demanding methods.
Keywords
Support Vector Machine Logistic Regression Support Vector Radial Basis Function Linear Discriminant AnalysisPreview
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