Evolution strategy based adaptive Lq penalty support vector machines with Gauss kernel for credit risk analysis

Authors:
Jianping Li;Gang Li;Dongxia Sun;Cheng-Few Lee
Affiliations:
Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, PR China;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, PR China and Graduate University of Chinese Academy of Sciences, Beijing 100049, PR China;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, PR China and Graduate University of Chinese Academy of Sciences, Beijing 100049, PR China;Department of Finance and Economics, Rutgers Business School, Rutgers University, Piscataway, NJ 08854, USA
Venue:
Applied Soft Computing
Year:
2012

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Abstract

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"qG-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"qG-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.