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A Least Squares Bilateral-Weighted Fuzzy SVM Method to Evaluate Credit Risk

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4 Author(s)
Wei Huang ; Sch. of Manage., Huazhong Univ. of Sci. & Technol., Wuhan ; Kin Keung Lai ; Lean Yu ; Shouyang Wang

In this study, we propose a least squares bilateral-weighted fuzzy support vector machine (LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization ability by treating each training sample as being both a possible good and bad customer and considering bilateral-weighted classification errors. For illustration purpose, a real-world credit risk assessment dataset is used to test the effectiveness of the LS-BFSV.M method.

Published in:

Natural Computation, 2008. ICNC '08. Fourth International Conference on  (Volume:7 )

Date of Conference:

18-20 Oct. 2008