Credit Risk Assessment with Least Squares Fuzzy Support Vector Machines

Author

Yu, Lean ; Lai, Kin Keung ; Wang, Shouyang

Author_Institution

Inst. of Syst. Sci., Chinese Acad. of Sci., Beijing

fYear

2006

fDate

18-22 Dec. 2006

Firstpage

823

Lastpage

827

Abstract

In this study, the authors discuss a least squares version of fuzzy support vector machine (FSVM) classifiers for designing a credit risk assessment system to discriminate good creditors from bad ones. Relative to the classical FSVM, the least squares FSVM (LS-FSVM) can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. For illustration, a real-world credit dataset is used to test the effectiveness of the LS-FSVM

Keywords

computational complexity; credit transactions; fuzzy reasoning; least squares approximations; linear programming; pattern classification; quadratic programming; risk management; support vector machines; FSVM; computational complexity; credit risk assessment; least squares fuzzy support vector machines; linear programming; quadratic programming; Artificial intelligence; Fuzzy systems; Least squares methods; Linear programming; Mathematics; Risk analysis; Risk management; Support vector machine classification; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on

Conference_Location

Hong Kong

Print_ISBN

0-7695-2702-7

Type

conf

DOI

10.1109/ICDMW.2006.54

Filename

4063739