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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References
Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23, 89–609 (1968)
Wiginton, J.C.: A note on the comparison of logit and discriminant models of consumer credit behaviour. Journal of Financial Quantitative Analysis 15, 757–770 (1980)
Grablowsky, B.J., Talley, W.K.: Probit and discriminant functions for classifying credit applicants: A comparison. Journal of Economic Business 33, 254–261 (1981)
Glover, F.: Improved Linear Programming Models for Discriminant Analysis. Decision Science 21, 771–785 (1990)
Henley, W.E., Hand, D.J.: A k-NN classifier for assessing consumer credit risk. Statistician 45, 77–95 (1996)
Yu, L., Wang, S.Y., Lai, K.K.: Credit risk assessment with a multistage neural network ensemble learning approach. Expert Systems with Applications 34(2), 1434–1444 (2008a)
Chen, M.C., Huang, S.H.: Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Systems with Applications 24, 433–441 (2003)
Yu, L., Wang, S.Y., Lai, K.K., Zhou, L.G.: Bio-Inspired Credit Risk Analysis - Computational Intelligence with Support Vector Machines. Springer, Berlin (2008b)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9(3), 293–300 (1999)
Zhou, L.G., Lai, K.K., Yu, L.: Credit scoring using support vector machines with direct search for parameters selection. Soft Computing 13, 149–155 (2009)
Yu, L., Wang, S.Y., Lai, K.K.: An Intelligent-Agent-Based Fuzzy Group Decision Making Model for Financial Multicriteria Decision Support: The Case of Credit Scoring. European Journal of Operational Research 195(3), 942–959 (2009)
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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. MCDM 2009. Communications in Computer and Information Science, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02298-2_84
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DOI: https://doi.org/10.1007/978-3-642-02298-2_84
Publisher Name: Springer, Berlin, Heidelberg
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