A Hybrid Approach for Effective Feature Selection using Neural Networks and Artificial Bee Colony Optimization

Feature selection (FS) is a fundamental pattern recognition problem, which aims to reduce the number of features used for recognition, with an acceptable accuracy. Unfortunately, the FS problem belongs to the set of NP-hard problems, that many approaches have been developed to solve it. In this paper a metaheuristic based on artificial bee colony optimization is presented to effective feature subset selection. Artificial bee colony is a novel optimization algorithm inspired of the natural behavior of honey bees in their search process for the best food sources. In this paper a new hybrid approach based on artificial neural networks and artificial bee colony optimization algorithm is proposed, that achieves an efficient feature subset. For evaluation of the selected subsets by the bees an artificial neural network was used as classifier. The predictive accuracy of selected subsets by the bees, and the length of the feature subset vector are considered as heuristic information for the bees. Obtained results show that proposed algorithm has better solution quality than the other wrapper and filter approaches. Keywords-feature subset selection; neural networks; artificial bee colony optimization; wrapper approaches;

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