Effective Feature Subset Identification Using Adaptive Bee Colony Algorithm

Abstract

The irrelevant features, along with redundant features, severely affect the accuracy of the learning machines. Feature subset selection as the process of identifying and removing many irrelevant and redundant features. The overall process of the optimal feature selection method is divided into two main steps, such as, i) preprocessing (ii) Optimal feature selection using clustering and tree generation. At first, preprocessing is done in the input micro array dataset. Then the Possibilistic fuzzy c-means clustering algorithm with optimal minimum spanning tree algorithm is applied on the high dimensional micro array dataset to select the important features. Here the proposed method is optimally select the features with the help of Adaptive artificial bee colony algorithm.