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CLC number: TP391.4

On-line Access: 2013-03-05

Received: 2012-06-28

Revision Accepted: 2013-01-22

Crosschecked: 2013-02-25

Cited: 1

Clicked: 5340

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.3 P.197-204

http://doi.org/10.1631/jzus.C1200205


Credit scoring by feature-weighted support vector machines


Author(s):  Jian Shi, Shu-you Zhang, Le-miao Qiu

Affiliation(s):  The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   yievans, qiulm@zju.edu.cn

Key Words:  Credit scoring model, Support vector machine (SVM), Feature weight, Random forest


Jian Shi, Shu-you Zhang, Le-miao Qiu. Credit scoring by feature-weighted support vector machines[J]. Journal of Zhejiang University Science C, 2013, 14(3): 197-204.

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author="Jian Shi, Shu-you Zhang, Le-miao Qiu",
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publisher="Zhejiang University Press & Springer",
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%I Zhejiang University Press & Springer
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T1 - Credit scoring by feature-weighted support vector machines
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A1 - Shu-you Zhang
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J0 - Journal of Zhejiang University Science C
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IS - 3
SP - 197
EP - 204
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200205


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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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