Abstract
In this research, we propose a novel method to find the relevant feature subset by using ant colony optimisation minimum-redundancy-maximum-relevance. The proposed approach considers the significance of each feature while reducing the dimensionality. The performance of proposed algorithm has been compared with existing biologically inspired feature subset selection algorithms. Eight datasets have been selected from UCI machine learning repository for experimentation. The experimental results indicate that the presented algorithm out performs the other algorithms in terms of the classification accuracy and feature reduction.