An evolution strategy-based multiple kernels multi-criteria programming approach: The case of credit decision making

Authors:
Jianping Li;Liwei Wei;Gang Li;Weixuan Xu
Affiliations:
Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, P.R. China;National Library of Standards, China National Institute of Standardization, Beijing 100088, P.R. China and Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, P.R. Chi ...;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, P.R. China and Graduate University of Chinese Academy of Sciences, Beijing 100039, P.R. China;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, P.R. China
Venue:
Decision Support Systems
Year:
2011

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Abstract

Credit risk analysis has long attracted a great deal of attention from both academic researchers and practitioners. However, because of the recent financial crisis, this field continues to draw ever increasingly attention. A multiple kernels multi-criteria programming approach based on evolution strategy (ES-MK-MCP) is proposed for credit decision making in this study. We introduce a linear combination of kernel functions to enhance the interpretability of credit classification models, and propose an alternative to optimize the parameters based on the evolution strategy. For illustration purpose, two UCI credit card data sets are used to verify the effectiveness and feasibility of the proposed model. As the experimental results reveal, the proposed ES-MK-MCP model is an efficient tool for credit risk analysis, especially for decision makers to identify the most relevant features.