32
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references39

          • Record: found
          • Abstract: not found
          • Article: not found

          Machine Learning.

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis

                Bookmark

                Author and article information

                Journal
                ScientificWorldJournal
                ScientificWorldJournal
                TSWJ
                The Scientific World Journal
                Hindawi Publishing Corporation
                2356-6140
                1537-744X
                2014
                10 September 2014
                : 2014
                : 795624
                Affiliations
                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
                Author notes

                Academic Editor: Shifei Ding

                Article
                10.1155/2014/795624
                4175386
                25295306
                a95ce0a0-2706-4a3c-a40f-d2cc8314cf02
                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.

                History
                : 20 June 2014
                : 5 August 2014
                : 5 August 2014
                Categories
                Research Article

                Uncategorized
                Uncategorized

                Comments

                Comment on this article