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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

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