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The Scientific World Journal
Volume 2014, Article ID 795624, 10 pages
Research Article

SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

1Department of Industrial Engineering and Management, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhong-Shan Road, Taiping District, Taichung 41170, Taiwan
2Department of Industrial Engineering & Management, National Chiao-Tung University, No. 1001, Ta-Hsueh Road, Hsinchu 300, Taiwan

Received 20 June 2014; Revised 5 August 2014; Accepted 5 August 2014; Published 10 September 2014

Academic Editor: Shifei Ding

Copyright © 2014 Mei-Ling Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters and to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.