A Relative Investigatory Analysis on Ant Colony algorithm and Genetic Algorithm for Feature Selection

  • Udit Narayan Kar Saurashtra University
  • Arunima Kar Institute of Technical education and Research
  • Anyatama Kar Institute of Technical education and Research,
Keywords: GA, ACO, Selection, Crossover, Mutation, Elitism

Abstract

Feature subset selection is used as a common technique in data pre-processing for pattern recognition, machine learning and data mining has attracted much attention in recent years. A good feature selection method can reduce the cost of feature measurement and increase classifier efficiency and classification accuracy. One approach in the feature selection area is employing populationbased optimization algorithms such as Genetic Algorithm (GA)-based method and Ant Colony Optimization (ACO) based method. In this paper ant colony optimization algorithm (ACO) is compared to Genetic algorithm (GA) using feature selection. Finally a comparative study is done to know the pros and cons of the work done

Author Biographies

Udit Narayan Kar, Saurashtra University

Computer Science Department

Saurashtra University

Arunima Kar, Institute of Technical education and Research

Computer Science Department

Institute of Technical education and Research

Anyatama Kar, Institute of Technical education and Research,

Computer Science Department

Institute of Technical education and Research

Published
2015-07-31
How to Cite
[1]
U. Kar, A. Kar, and A. Kar, “A Relative Investigatory Analysis on Ant Colony algorithm and Genetic Algorithm for Feature Selection”, EPH - International Journal of Science And Engineering (ISSN: 2454 - 2016), vol. 1, no. 3, pp. 21-31, Jul. 2015.