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
2012
Source PublicationAPPLIED SOFT COMPUTING
ISSN1568-4946
Volume12Issue:8Pages:8,2675-2682
AbstractCredit 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.
Other Abstract英文摘要
KeywordAdaptive Penalty Support Vector Machine Credit Risk Classification Evolution Strategy
Subject AreaComputer Science ; Interdisciplinary Applications ; Artificial Intelligence ; Computer Science
Indexed BySCI
Language英语
WOS IDWOS:000305275800067
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.casisd.cn/handle/190111/4223
Collection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)
Recommended Citation
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.
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