dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversity of Fortaleza
dc.date.accessioned2020-12-12T02:42:52Z
dc.date.accessioned2022-12-19T21:21:13Z
dc.date.available2020-12-12T02:42:52Z
dc.date.available2022-12-19T21:21:13Z
dc.date.created2020-12-12T02:42:52Z
dc.date.issued2020-09-01
dc.identifierApplied Soft Computing Journal, v. 94.
dc.identifier1568-4946
dc.identifierhttp://hdl.handle.net/11449/201828
dc.identifier10.1016/j.asoc.2020.106442
dc.identifier2-s2.0-85085731896
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5382462
dc.description.abstractFeature selection plays an essential role in machine learning since high dimensional real-world datasets are becoming more popular nowadays. The very basic idea consists in selecting a compact but representative set of features that reduce the computational cost and minimize the classification error. In this paper, the authors propose single, multi- and many-objective binary versions of the Artificial Butterfly Optimization (ABO) in the context of feature selection. The authors also propose two different approaches: (i) the first one (MO-I) aims at optimizing the classification accuracy of each class individually, while (ii) the second one (MO-II) considers the feature set minimization in the process either. The experiments were conducted over eight public datasets, and the proposed approaches are compared against the well-known Particle Swarm Optimization, Firefly Algorithm, Flower Pollination Algorithm, Brainstorm Optimization, and the Black Hole Algorithm. The results showed that the binary single-objective ABO performed better than the other meta-heuristic techniques, selecting fewer features and also figuring a lower computational burden. Concerning multi- and many-objective feature selection, both MO-I and MO-II approaches performed better than their single-objective meta-heuristic counterparts.
dc.languageeng
dc.relationApplied Soft Computing Journal
dc.sourceScopus
dc.subjectMachine learning
dc.subjectMany-objective optimization
dc.subjectMeta-heuristic algorithms
dc.subjectPattern recognition
dc.titleA multi-objective artificial butterfly optimization approach for feature selection
dc.typeArtículos de revistas


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