Evolution strategy based adaptive L-q penalty support vector machines with Gauss kernel for credit risk analysis
Li, JP; Li, G; Sun, DX; Lee, CF
发表期刊APPLIED SOFT COMPUTING
关键词Adaptive Penalty Support Vector Machine Credit Risk Classification Evolution Strategy
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.
2012
卷号12期号:8页码:8,2675-2682
ISSN1568-4946
学科领域Computer Science ; Interdisciplinary Applications ; Artificial Intelligence ; Computer Science
收录类别SCI
语种英语
WOS记录号WOS:000305275800067
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被引频次:11[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.casisd.cn/handle/190111/4223
专题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)
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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.
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