A Total Least Squares Proximal Support Vector Classifier for Credit Risk Evaluation
In this paper,a total least squares (TLS) version of proximal support vector machines (PSVM) is proposed for credit risk evaluation.The formulation of this new model is different from the original PSVM model,so a novel iterative algorithm is proposed to solve this model.A simulation test is first implemented on a classic two-spiral dataset,and then an empirical experiment is conducted on two public available credit datasets.The experiment results show that the proposed total least squares PSVM (TLS-PSVM) is at least comparable with PSVM and better than other models including standard SVM model.
Total Least Squares Technique Proximal Support Vector Machine Credit Risk Evaluation
Lean Yu Xiao Yao
School of Economics and Management,Beijing University of Chemical Technology Beijing 100029,China Academy of Mathematics and Systems Science,Chinese Academy of Sciences Beijing 100190,China
国内会议
开封
英文
237-246
2012-05-25（万方平台首次上网日期，不代表论文的发表时间）