NDLI: Credit scoring with F-score based on support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Weisong Chen Liang Shi
Copyright Year	2013
Description	Author affiliation: Autom. Dept., Shanghai Jiao Tong Univ., Shanghai, China (Weisong Chen; Liang Shi)
Abstract	Credit risk management is one of the most important issues in financial research. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications. In this article, a novel feature-weighted support vector machine credit scoring models are presented for credit risk assessment, in which F-score and improved F-score is adopted for feature importance calculating. These feature-weighted versions of Support Vector Machine are tested against the traditional feature selection Support Vector Machine on two real-world datasets and the research results reveal the validity of the proposed method. The feature-weighted methods have optimized performance, which improved the accuracy and reduced the modeling time consumption.
Starting Page	1512
Ending Page	1516
File Size	291484
Page Count	5
File Format	PDF
ISBN	9781479925643
e-ISBN	9781479925650
DOI	10.1109/MEC.2013.6885307
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Accuracy Measurement units Improved F-score Feature-weighted SVM Demensional reduce Educational institutions Stability analysis F-score SVM Kernel
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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