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, 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.