Abstract
Although many particle swarm optimization (PSO) based feature subset selection methods have been proposed, most of them seem to ignore the difference of feature subset selection problems and other optimization problems. We analyze the search process of a PSO based wrapper feature subset selection algorithm and find that characteristics of feature subset selection can be used to optimize this process. We compare wrapper and filter ways of evaluating features and define the domain knowledge of feature subset selection problems and we propose a fast wrapper feature subset selection algorithm based on PSO employed the domain knowledge of feature subset selection problems. Experimental results show that our method can work well, and the new algorithm can improve both the running time and the classification accuracy.
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