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A Modified Least Squares Support Vector Machine Classifier With Application To Credit Risk Analysis

Author

Listed:
  • LEAN YU

    (Institute of Systems Science Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;
    Research Center for Financial Engineering and Financial Management, Changsha 410114, China)

  • 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)

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.

Suggested Citation

  • Lean Yu & Shouyang Wang & Jie Cao, 2009. "A Modified Least Squares Support Vector Machine Classifier With Application To Credit Risk Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 697-710.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:04:n:s0219622009003600
    DOI: 10.1142/S0219622009003600
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    Cited by:

    1. Yu, Lean & Yao, Xiao & Zhang, Xiaoming & Yin, Hang & Liu, Jia, 2020. "A novel dual-weighted fuzzy proximal support vector machine with application to credit risk analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    2. Anqiang Huang & Kin Keung Lai & Han Qiao & Shouyang Wang & Zhenji Zhang, 2018. "Does Interval Knowledge Sharpen Forecasting Models? Evidence from China’s Typical Ports," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 467-483, March.
    3. Peng, Yi & Kou, Gang & Wang, Guoxun & Shi, Yong, 2011. "FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms," Omega, Elsevier, vol. 39(6), pages 677-689, December.

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