Feature selection using tabu search method

Selecting an optimal subset from original large feature set in the design of pattern classifier is an important and difficult problem. In this paper, we use tabu search to solve this feature selection problem and compare it with classic algorithms, such as sequential methods, branch and bound method...

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Bibliographic Details
Published inPattern recognition Vol. 35; no. 3; pp. 701 - 711
Main Authors Zhang, Hongbin, Sun, Guangyu
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.03.2002
Subjects
Curse of dimensionality
Feature selection
Pattern classifier
Search methods
Tabu search
Feature selection
Curse of dimensionality
Tabu search
Search methods
Pattern classifier
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Summary:Selecting an optimal subset from original large feature set in the design of pattern classifier is an important and difficult problem. In this paper, we use tabu search to solve this feature selection problem and compare it with classic algorithms, such as sequential methods, branch and bound method, etc., and most other suboptimal methods proposed recently, such as genetic algorithm and sequential forward (backward) floating search methods. Based on the results of experiments, tabu search is shown to be a promising tool for feature selection in respect of the quality of obtained feature subset and computation efficiency. The effects of parameters in tabu search are also analyzed by experiments.
ISSN:0031-3203
1873-5142
DOI:10.1016/S0031-3203(01)00046-2