An Efficient Feature Selection Using Ant Colony Optimization Algorithm

Authors:
Md. Monirul Kabir;Md. Shahjahan;Kazuyuki Murase
Affiliations:
Department of Human and Artificial Intelligence Systems, University of Fukui, Fukui, Japan 910-8507;Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh 9203;Department of Human and Artificial Intelligence Systems, University of Fukui, Fukui, Japan 910-8507
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Year:
2009

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Abstract

This paper presents an efficient feature selection algorithm by utilizing the strategy of ant colony optimization, called as ACOFS. Initially, ACOFS uses a modified framework to guide the ants in the right directions while constructing the graph (subset) paths. In the subsequent part, a set of new modified pheromone update rules as well as a set of new modified estimation of heuristic information for features are introduced. The effect of such modifications ultimately assists ants to generate salient feature subsets with reduced size. We evaluate the performance of ACOFS on four real-world benchmark datasets. The experimental results show that ACOFS has a remarkable capability to generate reduced size subsets of salient features with yielding significant classification accuracies.