A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful toot for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares Support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.