title: SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.
abstract: 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 C and γ 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.
authors.affiliation: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhong-Shan Road, Taiping District, Taichung 41170, Taiwan.
authors.forename: Mei-Ling
authors.initials: ML
authors.lastname: Huang
authors.affiliation: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhong-Shan Road, Taiping District, Taichung 41170, Taiwan.
authors.forename: Yung-Hsiang
authors.initials: YH
authors.lastname: Hung
authors.affiliation: Department of Industrial Engineering & Management, National Chiao-Tung University, No. 1001, Ta-Hsueh Road, Hsinchu 300, Taiwan.
authors.forename: W M
authors.initials: WM
authors.lastname: Lee
authors.affiliation: Department of Industrial Engineering & Management, National Chiao-Tung University, No. 1001, Ta-Hsueh Road, Hsinchu 300, Taiwan.
authors.forename: R K
authors.initials: RK
authors.lastname: Li
authors.affiliation: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhong-Shan Road, Taiping District, Taichung 41170, Taiwan.
authors.forename: Bo-Ru
authors.initials: BR
authors.lastname: Jiang
mesh_terms: D000818:Animals; D000836:Animals, Zoo / Q000145:classification*; D016208:Databases, Factual / Q000145:classification*; D003880:Dermatology / Q000145:classification* / Q000706:statistics & numerical data; D006801:Humans; D060388:Support Vector Machine