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Cutting-Edge Research Topics on Multiple Criteria Decision Making

Volume 35 of the series Communications in Computer and Information Science pp 573-579

Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

  • Lean YuAffiliated withInstitute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
  • , Shouyang WangAffiliated withInstitute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
  • , K. K. LaiAffiliated withDepartment of Management Sciences, City University of Hong Kong

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Abstract

Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.