SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier
Joint Authors
Huang, Mei-Ling
Hung, Yung-Hsiang
Lee, W. M.
Jiang, Bo-Ru
Li, Rong-Kwei
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Huang, Mei-Ling& Hung, Yung-Hsiang& Lee, W. M.& Li, Rong-Kwei& Jiang, Bo-Ru. 2014. SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1051062
Modern Language Association (MLA)
Huang, Mei-Ling…[et al.]. SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier. The Scientific World Journal No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1051062
American Medical Association (AMA)
Huang, Mei-Ling& Hung, Yung-Hsiang& Lee, W. M.& Li, Rong-Kwei& Jiang, Bo-Ru. SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1051062
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051062