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

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  • Lean Yu

Abstract

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.

Suggested Citation

  • Lean Yu, 2014. "Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-9, June.
    • Handle: RePEc:hin:jnddns:564213
    DOI: 10.1155/2014/564213
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    Cited by:

    1. Agustin Pérez-Martín & Agustin Pérez-Torregrosa & Alejandro Rabasa & Marta Vaca, 2020. "Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans," Mathematics, MDPI, vol. 8(11), pages 1-16, November.

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