Investigating the effect of fixing the subset length on the performance of ant colony optimization for feature selection for supervised learning

This paper studies the effect of fixing the length of the selected feature subsets on the performance of ant colony optimization (ACO) for feature selection (FS) for supervised learning. It addresses this concern by investigating: (1) determining the optimal feature subset from datamining perspective, (2) demonstrating the solution convergence in case of fixing the length of the selected feature subsets, (3) determining the subset length in ACO for subset selection problems, and (4) different stopping criteria when solving FS by ACO. Besides, two types of experiments on ACO algorithms for FS for classification and regression problems using artificial and real world datasets in two cases fixing and not fixing the length of the selected feature subsets with the use of a support vector machine. The obtained results showed that not fixing the length of the selected feature subsets is better than fixing the length of the selected feature subsets.