Effect of local measure on performance of the ant feature selection algorithm

The ant feature selection algorithm has recently been proposed as a new method for feature subset selection. It uses measures of both local feature importance and overall performance of subsets to search the feature space for optimal solutions. In this paper, we evaluate the effect of different local importance measures; namely the Fisher Criterion, the Mutual Information based Feature Selection, and the Mutual Information Evaluation Function.