DCA based algorithms for feature selection in multi-class support vector machine
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- 作者：Hoai An Le Thi ; Manh Cuong Nguyen
- 关键词：Feature selection ; MSVM ; DC programming ; DCA ; DC approximation ; Exact penalty
- 刊名：Annals of Operations Research
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：249
- 期：1-2
- 页码：273-300
- 全文大小：
- 刊物类别：Business and Economics
- 刊物主题：Operation Research/Decision Theory; Combinatorics; Theory of Computation;
- 出版者：Springer US
- ISSN：1572-9338
- 卷排序：249
文摘
This paper addresses the problem of feature selection for Multi-class Support Vector Machines. Two models involving the \(\ell _{0}\) (the zero norm) and the \(\ell _{2}\)–\(\ell _{0}\) regularizations are considered for which two continuous approaches based on DC (Difference of Convex functions) programming and DCA (DC Algorithms) are investigated. The first is DC approximation via several sparse inducing functions and the second is an exact reformulation approach using penalty techniques. Twelve versions of DCA based algorithms are developed on which empirical computational experiments are fully performed. Numerical results on real-world datasets show the efficiency and the superiority of our methods versus one of the best standard algorithms on both feature selection and classification.