dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorMiddlesex University
dc.date.accessioned2014-05-27T11:30:30Z
dc.date.accessioned2022-10-05T18:57:48Z
dc.date.available2014-05-27T11:30:30Z
dc.date.available2022-10-05T18:57:48Z
dc.date.created2014-05-27T11:30:30Z
dc.date.issued2013-08-29
dc.identifierSwarm Intelligence and Bio-Inspired Computation, p. 225-237.
dc.identifierhttp://hdl.handle.net/11449/76352
dc.identifier10.1016/B978-0-12-405163-8.00009-0
dc.identifier2-s2.0-84883929298
dc.identifier9039182932747194
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3925242
dc.description.abstractFeature selection aims to find the most important information to save computational efforts and data storage. We formulated this task as a combinatorial optimization problem since the exponential growth of possible solutions makes an exhaustive search infeasible. In this work, we propose a new nature-inspired feature selection technique based on bats behavior, namely, binary bat algorithm The wrapper approach combines the power of exploration of the bats together with the speed of the optimum-path forest classifier to find a better data representation. Experiments in public datasets have shown that the proposed technique can indeed improve the effectiveness of the optimum-path forest and outperform some well-known swarm-based techniques. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.
dc.languageeng
dc.relationSwarm Intelligence and Bio-Inspired Computation
dc.rightsAcesso restrito
dc.sourceScopus
dc.subjectBat algorithm
dc.subjectFeature selection
dc.subjectMetaheuristic algorithms
dc.subjectOptimum-path forest classifier
dc.subjectPattern classification
dc.titleBinary Bat Algorithm for Feature Selection
dc.typeCapítulo de livro


Este ítem pertenece a la siguiente institución