Abstract:
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.
