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

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

Author(s) -

Lean Yu

Publication year - 2014

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2014/564213

Subject(s) - outlier , computer science , support vector machine , fuzzy logic , least squares support vector machine , least squares function approximation , robustness (evolution) , classifier (uml) , artificial intelligence , machine learning , data mining , generalization , pattern recognition (psychology) , mathematics , statistics , mathematical analysis , biochemistry , chemistry , estimator , gene

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 toadd generalization capability and outlier insensitivity, while the least squares method is adopted to reduce the computational complexity. For illustrativepurposes, a real-world credit risk dataset is used to test the effectiveness and robustness of the proposed LS-FSVM methodology

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