A Study on Meta Heuristic Algorithms for Feature Selection

Abstract

Vast number of feature selection methods are available to evaluate the best subset of features as the data in the real world is growing bigger and bigger. Feature selection methods are essential in order to reduce the processing time of the model, improving the accuracy of the model, and to avoid the problem of curse of dimensionality. The objective of this paper is to elaborate feature selection which is essential for classification and prediction tasks. We concentrate on meta heuristic algorithms for optimal feature subset selection. We also made a comparison of the characteristics of meta heuristic algorithms.