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

• Published in: Decision Support Systems Vol. 51; no. 2; pp. 292 - 298
• Main Authors: Li, Jianping, Wei, Liwei, Li, Gang, Xu, Weixuan
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2011; Elsevier Sequoia S.A
• Subjects: Classification; Credit risk; Decision making; Evolution; Evolution strategies; Kernels; Mathematical programming; Multi-criteria programming; Multiple criteria decision making; Multiple kernels; Optimization algorithms; Parameter optimization; Programming; Risk analysis; Risk assessment; Strategy; Studies; Multi-criteria programming; Credit risk; Decision making; Evolution strategies; Multiple kernels
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2010.11.022

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.