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Evolution strategy based adaptive L-q penalty support vector machines with Gauss kernel for credit risk analysis
文献类型：期刊论文
| 作者 | Li, JP ; Li, G ; Sun, DX ; Lee, CF |
| 刊名 | APPLIED SOFT COMPUTING
 |
| 出版日期 | 2012 |
| 卷号 | 12期号:8页码:8,2675-2682 |
| 关键词 | Adaptive penalty Support vector machine Credit risk classification Evolution strategy |
| ISSN号 | 1568-4946 |
| 中文摘要 | Credit risk analysis has long attracted great attention from both academic researchers and practitioners. However, the recent global financial crisis has made the issue even more important because of the need for further enhancement of accuracy of classification of borrowers. In this study an evolution strategy (ES) based adaptive L-q SVM model with Gauss kernel (ES-AL(q)G-SVM) is proposed for credit risk analysis. Support vector machine (SVM) is a classification method that has been extensively studied in recent years. Many improved SVM models have been proposed, with non-adaptive and pre-determined penalties. However, different credit data sets have different structures that are suitable for different penalty forms in real life. Moreover, the traditional parameter search methods, such as the grid search method, are time consuming. The proposed ES-based adaptive L-q SVM model with Gauss kernel (ES-AL(q)G-SVM) aims to solve these problems. The non-adaptive penalty is extended to (0, 2] to fit different credit data structures, with the Gauss kernel, to improve classification accuracy. For verification purpose, two UCI credit datasets and a real-life credit dataset are used to test our model. The experiment results show that the proposed approach performs better than See5, DT, MCCQP, SVM light and other popular algorithms listed in this study, and the computing speed is greatly improved, compared with the grid search method. (C) 2012 Elsevier B. V. All rights reserved. |
| 英文摘要 | 英文摘要 |
| 学科主题 | Computer Science ; Artificial Intelligence; Computer Science ; Interdisciplinary Applications |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000305275800067 |
| 公开日期 | 2012-11-12 |
| 源URL | [http://ir.casipm.ac.cn/handle/190111/4223]  |
| 专题 | 科技战略咨询研究院_中国科学院科技政策与管理科学研究所(1985年6月-2015年12月) |
| 推荐引用方式 GB/T 7714 | Li, JP,Li, G,Sun, DX,et al. Evolution strategy based adaptive L-q penalty support vector machines with Gauss kernel for credit risk analysis[J]. APPLIED SOFT COMPUTING,2012,12(8):8,2675-2682. |
| APA | Li, JP,Li, G,Sun, DX,&Lee, CF.(2012).Evolution strategy based adaptive L-q penalty support vector machines with Gauss kernel for credit risk analysis.APPLIED SOFT COMPUTING,12(8),8,2675-2682. |
| MLA | Li, JP,et al."Evolution strategy based adaptive L-q penalty support vector machines with Gauss kernel for credit risk analysis".APPLIED SOFT COMPUTING 12.8(2012):8,2675-2682. |
入库方式： OAI收割
来源：科技战略咨询研究院
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