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Front. Inform. Technol. Electron. Eng.  2013, Vol. 14 Issue (3): 197-204    DOI: 10.1631/jzus.C1200205
    
Credit scoring by feature-weighted support vector machines
Jian Shi, Shu-you Zhang, Le-miao Qiu
The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China; School of Electrical and Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China
Credit scoring by feature-weighted support vector machines
Jian Shi, Shu-you Zhang, Le-miao Qiu
The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China; School of Electrical and Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China
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摘要： Recent finance and debt crises have made credit risk management one of the most important issues in financial research. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. In this paper, a novel feature-weighted support vector machine (SVM) credit scoring model is presented for credit risk assessment, in which an F-score is adopted for feature importance ranking. Considering the mutual interaction among modeling features, random forest is further introduced for relative feature importance measurement. These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.
关键词： Credit scoring model;  Support vector machine (SVM);  Feature weight;  Random forest    
Abstract: Recent finance and debt crises have made credit risk management one of the most important issues in financial research. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. In this paper, a novel feature-weighted support vector machine (SVM) credit scoring model is presented for credit risk assessment, in which an F-score is adopted for feature importance ranking. Considering the mutual interaction among modeling features, random forest is further introduced for relative feature importance measurement. These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.
Key words: Credit scoring model    Support vector machine (SVM)    Feature weight    Random forest
收稿日期: 2012-06-28 出版日期: 2013-03-05
CLC:  TP391.4  
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Jian Shi, Shu-you Zhang, Le-miao Qiu. Credit scoring by feature-weighted support vector machines. Front. Inform. Technol. Electron. Eng., 2013, 14(3): 197-204.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1200205    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2013/V14/I3/197

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