An improved branch and bound algorithm for feature selection

Authors:
Xue-wen Chen
Affiliations:
Department of Electrical and Computer Engineering, California State University, 18111 Nordhoff Street, Northridge, CA
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

Feature selection plays an important role in pattern classification. In this paper, we present an improved branch and bound algorithm for Optimal feature subset selection. This algorithm searches for an optimal solution in a large solution tree in an efficient manner by cutting unnecessary paths which are guaranteed not to contain the optimal solution. Our experimental results demonstrate the effectiveness of the new algorithm.