An Ant Colony Optimization Based Approach for Feature Selection


This paper presents a new feature subset selection algorithm based on the Ant Colony Optimization (ACO). ACO is a metaheuristic inspired by the behaviour of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by utilizing distributed computing, local heuristics and previous knowledge. We modified the ACO algorithm so that it can be used to search for the best subsets of features. A new pheromone trail update formula is presented, and the various parameters that lead to better convergence are tested. Results on speech classification problem show that the proposed algorithm achieves better performance than both greedy and genetic algorithm based feature selection methods

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OPUS - University of Technology Sydney

Last time updated on 14/09/2015

This paper was published in OPUS - University of Technology Sydney.

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