| 论文题目 | Credit Risk Evaluation Using ES Based SVM-MK |
| 作者 | 魏利伟,张莺 |
| 年度 | 2016 |
| 发表/出版时间 | 2016/9/7 |
| 发表期刊/会议 | 5th International Conference on Measurement, Instrumentation and Automation |
| 关键词 | Credit risk evaluation, SVM-MK, ES SVM-MK,SVM |
| 摘要 | Under the background of big data recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for credit risk evaluation, such as SVM. In this study, we discuss the applications of the evolution strategies based support vector machine with mixture of kernel(ES based SVM-MK) to design a credit evaluation system, which can discriminate good creditors from bad ones. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. A real life credit dataset from a US commercial bank is used to demonstrate the good performance of the ES SVM- MK. |