dc.contributor | Universidade Estadual Paulista (Unesp) | |
dc.contributor | Middlesex University | |
dc.date.accessioned | 2014-05-27T11:30:30Z | |
dc.date.accessioned | 2022-10-05T18:57:48Z | |
dc.date.available | 2014-05-27T11:30:30Z | |
dc.date.available | 2022-10-05T18:57:48Z | |
dc.date.created | 2014-05-27T11:30:30Z | |
dc.date.issued | 2013-08-29 | |
dc.identifier | Swarm Intelligence and Bio-Inspired Computation, p. 225-237. | |
dc.identifier | http://hdl.handle.net/11449/76352 | |
dc.identifier | 10.1016/B978-0-12-405163-8.00009-0 | |
dc.identifier | 2-s2.0-84883929298 | |
dc.identifier | 9039182932747194 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/3925242 | |
dc.description.abstract | Feature 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.language | eng | |
dc.relation | Swarm Intelligence and Bio-Inspired Computation | |
dc.rights | Acesso restrito | |
dc.source | Scopus | |
dc.subject | Bat algorithm | |
dc.subject | Feature selection | |
dc.subject | Metaheuristic algorithms | |
dc.subject | Optimum-path forest classifier | |
dc.subject | Pattern classification | |
dc.title | Binary Bat Algorithm for Feature Selection | |
dc.type | Capítulo de livro | |