| 论文题目 | Credit Risk Evaluation Using: Least Squares Support Vector Machine with Mixture |
| 作者 | 魏利伟,李文武 |
| 年度 | 2016 |
| 发表/出版时间 | 2016/9/28 |
| 发表期刊/会议 | 2016 International Conference on Network and Information Systems for Computers |
| 关键词 | credit evaluation; LS-SVM-MK; mixture kernel; |
| 摘要 | Credit risk evaluation under the background of big data has been the major focus of financial and banking industry due to recent financial crises. Recent studies have revealed that emerging modern optimization techniques are advantageous to statistical models for credit risk evaluation, such as LS-SVM. In this paper, a least squares support vector machine with mixture kernel (LS-SVM-MK) is proposed to solve the problem of the traditional LS-SVM model, such as the loss of sparseness and robustness. Thus that will result in slow testing speed and poor generalization performance. The revision model LS-SVM-MK is equivalent to solve a linear equation set with deficient rank just like the over complete problem in independent component analysis. A minimum of 1- penalty based object function is chosen to get the sparse and robust solution. Some credit card datasets are used to demonstrate the effectiveness of this model. The experimental results show that LS-SVM-MK can obtain a small number of features and improve the generalization ability of LS-SVM. |