Credit Risk Assessment with Least Squares Fuzzy Support Vector Machines

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

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