Metaheuristic Approach for Efficient Feature Selection: A Data Classification Perspective

Background/Objectives: Feature selection has been an active research area in pattern recognition, statistics, and data mining communities. The main objective of the proposed feature selection method is to choose a subset of input variables by eliminating features with little or no predictive information using Meta -heuristic approach. Methods/Statistical Analysis: The presented method uses genetic algorithm for selecting the optimal feature subset from the datasets. Findings: The purpose of this method is to reduce the dimension of the original thereby improves classification accuracy of the selected feature subsets. The experiment performed with various standard dataset revealed that the proposed method is superior to most of the existing feature selection methods in terms of feature subset selection, classification accuracy and running time.