DocumentCode :
2223091
Title :
Comparison of feature selection approaches based on the SVM classification
Author :
Li, F.C. ; Chen, F.L. ; Wang, G.E.
Author_Institution :
Dept. of Ind. Eng. & Eng. Manage., Univ. of Tsing Hua, Hsinchu, Taiwan
fYear :
2008
fDate :
8-11 Dec. 2008
Firstpage :
400
Lastpage :
404
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.
Keywords :
decision trees; feature extraction; pattern classification; rough set theory; support vector machines; F-score models; SVM classification; Wilcoxon signed rank test; credit scoring; data mining; decision tree; feature selection approaches; linear discriminate analysis; rough set; Data mining; Decision making; Decision trees; Filters; Industrial engineering; Linear discriminant analysis; Machine learning; Support vector machine classification; Support vector machines; Testing; Decision tree; F-score; Linear discriminate analysis; Rough set; Support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management, 2008. IEEM 2008. IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-2629-4
Electronic_ISBN :
978-1-4244-2630-0
Type :
conf
DOI :
10.1109/IEEM.2008.4737899
Filename :
4737899
Link To Document :
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