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dc.contributor.authorAljarah, Ibrahim
dc.contributor.authorAl-Zoubi, Ala´ M.
dc.contributor.authorCastillo Valdivieso, Pedro Ángel 
dc.contributor.authorMerelo Guervos, Juan Julián 
dc.date.accessioned2021-09-17T08:04:31Z
dc.date.available2021-09-17T08:04:31Z
dc.date.issued2021-07-14
dc.identifier.citationI. Aljarah... [et al.], "A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization," in IEEE Access, vol. 9, pp. 100009-100028, 2021, doi: [10.1109/ACCESS.2021.3097206]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/70257
dc.descriptionThis work was supported in part by the Ministerio de Economia y Competitividad under Grant TIN2017-85727-C4-2P.es_ES
dc.description.abstractClassification tasks often include, among the large number of features to be processed in the datasets, many irrelevant and redundant ones, which can even decrease the ef ciency of classi ers. Feature Selection (FS) is the most common preprocessing technique utilized to overcome the drawbacks of the high dimensionality of datasets and often has two con icting objectives: The rst function aims to maximize the classi cation performance or reduce the error rate of the classi er. In contrast, the second function is designed to minimize the number of features. However, the majority of wrapper FS techniques are developed for single-objective scenarios. Multi-verse optimizer (MVO) is considered as one of the well-regarded optimization approaches in recent years. In this paper, the binary multi-objective variant of MVO (MOMVO) is proposed to deal with feature selection tasks. The standard MOMVO suffers from local optima stagnation, so we propose an improved binary MOMVO to deal with this issue using the memory concept and personal best of the universes. The experimental results and comparisons indicate that the proposed binary MOMVO approach can effectively eliminate irrelevant and/or redundant features and maintain a minimum classi cation error rate when dealing with different datasets compared with the most popular feature selection techniques. Furthermore, the 14 benchmark datasets showed that the proposed approach outperforms the stat-of-art multi-objective optimization algorithms for feature selection.es_ES
dc.description.sponsorshipSpanish Government TIN2017-85727-C4-2Pes_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectWrapper feature selectiones_ES
dc.subjectMulti-verse algorithmes_ES
dc.subjectOptimizationes_ES
dc.subjectClassification es_ES
dc.titleA Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimizationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1109/ACCESS.2021.3097206
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España