Evolutionary Approach to Feature Selection with Associative Models


Feature selection aims to nd ways to single out the subset of features which best represents the phenomenon at hand and improves performance. This paper presents an approach based on evolutionary computation and the associative paradigm for classi cation. A wrapperstyle search guided by a genetic algorithm uses the Hybrid Associative Classi er to evaluate candidate solutions and thus approximate the optimal feature subset for di erent data sets. The results suggest that this is a feasible approach for feature selection, obtaining solutions equal or similar to the optimal solution while evaluating a relatively small fraction of the search space.

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@article{FerreiraSantiago2014EvolutionaryAT, title={Evolutionary Approach to Feature Selection with Associative Models}, author={{\'A}ngel Ferreira-Santiago and Cornelio Y{\'a}{\~n}ez-M{\'a}rquez and Mario Aldape-P{\'e}rez and Itzam{\'a} L{\'o}pez-Y{\'a}{\~n}ez}, journal={Research in Computing Science}, year={2014}, volume={78}, pages={111-122} }