NDLI: Comparison of feature selection approaches based on the SVM classification

Content Provider	IEEE Xplore Digital Library
Author	Li, F.C. Chen, F.L. Wang, G.E.
Copyright Year	2008
Description	Author affiliation: Dept. of Inf. & Manage., Junior Med. Coll. of Jen-Teh, Miaoli, Taiwan (Wang, G.E.) \|\| Dept. of Ind. Eng. & Eng. Manage., Univ. of Tsing Hua, Hsinchu, Taiwan (Li, F.C.; Chen, F.L.)
Abstract	The credit scoring has been regarded as a critical topic. Creating an effective classificatory model will objectively help managers instead of intuitive experience. This study proposed four strategies combining with the SVM (support vector machine) classifier for features selection that retains sufficient information for classification purpose. Different features preprocessing steps were constructed with four strategies of conventional Linear discriminate analysis (LDA), Decision tree, Rough set and F-score models to optimize feature space by removing both irrelevant and redundant features. The accuracy of four models are compared and nonparametric Wilcoxon signed rank test was held to show the significant difference between these models. Our results suggest that hybrid credit scoring models can mostly classify the applicants as either good or bad clients that are robust and effective in finding optimal subsets and are a promising method to the fields of data mining.
Starting Page	400
Ending Page	404
File Size	714347
Page Count	5
File Format	PDF
ISBN	9781424426294
DOI	10.1109/IEEM.2008.4737899
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision making Industrial engineering F-score Support vector machine Data mining Decision tree Rough set Support vector machines Filters Support vector machine classification Machine learning Linear discriminant analysis Decision trees Testing Linear discriminate analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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