BBA: A binary bat algorithm for feature selection

• Autor(a) principal: Nakamura, R. Y M [UNESP]
• Data de Publicação: 2012
• Outros Autores: Pereira, L. A M [UNESP], Costa, K. A. [UNESP], Rodrigues, D. [UNESP], Papa, João Paulo [UNESP], Yang, X. S.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/SIBGRAPI.2012.47; http://hdl.handle.net/11449/73832
• topic: bat algorithm; feature selection; optimum-path forest; Data sets; Exhaustive search; Optimization problems; Optimum-path forests; Selection techniques; Wrapper approach; Feature extraction; Forestry; Algorithms; Automatic Control; Optimization; Problem Solving; Techniques

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper approach combines the power of exploration of the bats together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in five public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based techniques. © 2012 IEEE.