A MODIFIED LEAST SQUARES SUPPORT VECTOR MACHINE CLASSIFIER WITH APPLICATION TO CREDIT RISK ANALYSIS

Lean Yu (), Shouyang Wang and Jie Cao
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Shouyang Wang: Institute of Systems Science Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Jie Cao: School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China

International Journal of Information Technology & Decision Making (IJITDM), 2009, vol. 08, issue 04, 697-710

Abstract: In this paper, a modified least squares support vector machine classifier, called theC-variable least squares support vector machine (C-VLSSVM) classifier, is proposed for credit risk analysis. The main idea of the proposed classifier is based on the prior knowledge that different classes may have different importance for modeling and more weight should be given to classes having more importance. TheC-VLSSVM classifier can be obtained by a simple modification of the regularization parameter, based on the least squares support vector machine (LSSVM) classifier, whereby more weight is given to errors in classification of important classes, than to errors in classification of unimportant classes, while keeping the regularized terms in their original form. For illustration purpose, two real-world credit data sets are used to verify the effectiveness of theC-VLSSVM classifier. Experimental results obtained reveal that the proposedC-VLSSVM classifier can produce promising classification results in credit risk analysis, relative to other classifiers listed in this study.

Keywords: Least squares support vector machine classifier; regularization parameter; prior knowledge; credit risk analysis (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0219622009003600

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