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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: 4865

Citations:  Bibtex RefMan EndNote GB/T7714

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


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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%T Credit scoring by feature-weighted support vector machines
%A Jian Shi
%A Shu-you Zhang
%A Le-miao Qiu
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%DOI 10.1631/jzus.C1200205

T1 - Credit scoring by feature-weighted support vector machines
A1 - Jian Shi
A1 - Shu-you Zhang
A1 - Le-miao Qiu
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 3
SP - 197
EP - 204
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1200205

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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