| 论文题目 | A New Method Evaluating Credit Risk with ES Based LS-SVM-MK |
| 作者 | 魏利伟,李文武,张莺 |
| 年度 | 2017 |
| 发表/出版时间 | 2017/11/26 |
| 发表期刊/会议 | Computer Science and Engineering |
| 关键词 | LS-SVM, SVM, ES based L1-LS-SVM, Text classification |
| 摘要 | The era of big data is here. Recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for credit risk evaluation, such as SVM, LS-SVM. In this paper we discuss the applications of the evolution strategies based least squares support vector machine with mixture of kernel (ES based LS-SVM-MK) to design a credit evaluation system, which can discriminate good creditors from bad ones. Differing from the standard LS-SVM, the LS-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 based LS-SVM-MK. |