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Discrete Dynamics in Nature and Society
Volume 2014, Article ID 564213, 9 pages
Research Article

Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier

Lean Yu1,2

1Alibaba Business College, Hangzhou Normal University, Hangzhou 310036, China
2School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China

Received 12 February 2014; Accepted 27 May 2014; Published 12 June 2014

Academic Editor: Fenghua Wen

Copyright © 2014 Lean Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A least squares fuzzy support vector machine (LS-FSVM) model that integrates advantages of fuzzy support vector machine (FSVM) and least squares method is proposed for credit risk evaluation. In the proposed LS-FSVM model, the purpose of incorporating the concepts of fuzzy sets is to add generalization capability and outlier insensitivity, while the least squares method is adopted to reduce the computational complexity. For illustrative purposes, a real-world credit risk dataset is used to test the effectiveness and robustness of the proposed LS-FSVM methodology.