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: 7304
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.
@article{title="Credit scoring by feature-weighted support vector machines",
author="Jian Shi, Shu-you Zhang, Le-miao Qiu",
journal="Journal of Zhejiang University Science C",
volume="14",
number="3",
pages="197-204",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1200205"
}
%0 Journal Article
%T Credit scoring by feature-weighted support vector machines
%A Jian Shi
%A Shu-you Zhang
%A Le-miao Qiu
%J Journal of Zhejiang University SCIENCE C
%V 14
%N 3
%P 197-204
%@ 1869-1951
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1200205
TY - JOUR
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
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.
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