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

  • Lean Yu
  • Shouyang Wang
  • K. K. Lai
Conference paper

DOI: 10.1007/978-3-642-02298-2_84

Part of the Communications in Computer and Information Science book series (CCIS, volume 35)
Cite this paper as:
Yu L., Wang S., Lai K.K. (2009) Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model. In: Shi Y., Wang S., Peng Y., Li J., Zeng Y. (eds) Cutting-Edge Research Topics on Multiple Criteria Decision Making. Communications in Computer and Information Science, vol 35. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lean Yu
    • 1
  • Shouyang Wang
    • 1
  • K. K. Lai
    • 2
  1. 1.Institute of Systems Science, Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  2. 2.Department of Management SciencesCity University of Hong KongHong KongChina

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