Feature selection for support vector machines using Generalized Benders Decomposition

•We formulated two versions of the feature selection problem.•We developed an exact algorithm that solves the feature selection problem.•We provided details about the algorithm and its convergence properties.•The method is more accurate than benchmark methods. We propose an exact method, based on Ge...

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Bibliographic Details
Published in:European journal of operational research 2015-07, Vol.244 (1), p.210-218
Main Author: Aytug, Haldun
Format: Article
Language:English
Subjects:
Benders decomposition
Data mining
Feature extraction
Feature selection
Generalized Benders Decomposition
Mathematical programming
Operational research
Statistical methods
Studies
Support vector machines
Support vector machines (SVM)
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Description
Summary:•We formulated two versions of the feature selection problem.•We developed an exact algorithm that solves the feature selection problem.•We provided details about the algorithm and its convergence properties.•The method is more accurate than benchmark methods. We propose an exact method, based on Generalized Benders Decomposition, to select the best M features during induction. We provide details of the method and highlight some interesting parallels between the technique proposed here and some of those published in the literature. We also propose a relaxation of the problem where selecting too many features is penalized. The original method performs well on a variety of data sets. The relaxation, though competitive, is sensitive to the penalty parameter.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2015.01.006