Prediction model building with clustering-launched classification and support vector machines in credit scoring

Authors:
Shu-Ting Luo;Bor-Wen Cheng;Chun-Hung Hsieh
Affiliations:
Graduate School of Industry Engineering and Management, National Yunlin University of Science and Technology, 123 University Road, Section , Douliou, Yunlin 64002, Taiwan;Graduate School of Industry Engineering and Management, National Yunlin University of Science and Technology, 123 University Road, Section , Douliou, Yunlin 64002, Taiwan;National Taichung Institute of Technology, 129 Section 3, San-min Road, Taichung 40401, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Recently, credit scoring has become a very important task as credit cards are now widely used by customers. A method that can accurately predict credit scoring is greatly needed and good prediction techniques can help to predict credit more accurately. One powerful classifier, the support vector machine (SVM), was successfully applied to a wide range of domains. In recent years, researchers have applied the SVM-based in the prediction of credit scoring, and the results have been shown it to be effective. In this study, two real world credit datasets in the University of California Irvine Machine Learning Repository were selected. SVM and a new classifier, clustering-launched classification (CLC), were employed to predict the accuracy of credit scoring. The advantages of using CLC are that it can classify data efficiently and only need one parameter needs to be decided. In substance, the results show that CLC is better than SVM. Therefore, CLC is an effective tool to predict credit scoring.