Metaheuristic Approach for Efficient Feature Selection: A Data Classification Perspective

B  Amarnath    and S  Appavu alias Balamurugan

doi:10.17485/ijst/2016/v9i4/87039

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Metaheuristic Approach for Efficient Feature Selection: A Data Classification Perspective

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2016/v9i4/87039

Year: 2016, Volume: 9, Issue: 4, Pages: 1-6

Original Article

Metaheuristic Approach for Efficient Feature Selection: A Data Classification Perspective

B. Amarnath^1* and S. Appavu alias Balamurugan²

¹M. S. University, Tirunelveli - 627012, Tamil Nadu, India; [email protected] ²Department of I. T., KLNCIT, Sivagangai - 630612, Tamil Nadu, India; [email protected]

*Author For Correspondence
B. Amarnath M. S. University, Tirunelveli - 627012, Tamil Nadu, India; [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

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.

Keywords: Classification and Genetic Algorithms, Data Mining, Feature Selection