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dc.contributor.authorBolón-Canedo, Verónica
dc.contributor.authorRego-Fernández, Diego
dc.contributor.authorPeteiro Barral, Diego
dc.contributor.authorAlonso-Betanzos, Amparo
dc.contributor.authorGuijarro-Berdiñas, Bertha
dc.contributor.authorSánchez-Maroño, Noelia
dc.date.accessioned2024-05-03T17:59:39Z
dc.date.available2024-05-03T17:59:39Z
dc.date.issued2018
dc.identifier.citationBolón-Canedo, V., Rego-Fernández, D., Peteiro-Barral, D. et al. On the scalability of feature selection methods on high-dimensional data. Knowl Inf Syst 56, 395–442 (2018). https://doi.org/10.1007/s10115-017-1140-3es_ES
dc.identifier.issn0219-1377
dc.identifier.issn0219-3116
dc.identifier.urihttp://hdl.handle.net/2183/36408
dc.descriptionThis version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10115-017-1140-3.es_ES
dc.description.abstract[Abstract]: Lately, derived from the explosion of high dimensionality, researchers in machine learning became interested not only in accuracy, but also in scalability. Although scalability of learning methods is a trending issue, scalability of feature selection methods has not received the same amount of attention. This research analyzes the scalability of state-of-the-art feature selection methods, belonging to filter, embedded and wrapper approaches. For this purpose, several new measures are presented, based not only on accuracy but also on execution time and stability. The results on seven classical artificial datasets are presented and discussed, as well as two cases study analyzing the particularities of microarray data and the effect of redundancy. Trying to check whether the results can be generalized, we included some experiments with two real datasets. As expected, filters are the most scalable feature selection approach, being INTERACT, ReliefF and mRMR the most accurate methods.es_ES
dc.description.sponsorshipThis research has been economically supported in part by the Ministerio de Economía y Competitividad of the Spanish Government (research project TIN2015-65069-C2-1-R), by European Union FEDER funds and by the Consellería de Industria of the Xunta de Galicia (research project GRC2014/035). Financial support from the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2016-2019) and the European Union (European Regional Development Fund - ERDF), is gratefully acknowledged (research project ED431G/01).es_ES
dc.description.sponsorshipXunta de Galicia; GRC2014/035es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2015-65069-C2-1-R/ES/ALGORITMOS ESCALABLES DE APRENDIZAJE COMPUTACIONAL: MAS ALLA DE LA CLASIFICACION Y LA REGRESIONes_ES
dc.relation.urihttps://doi.org/10.1007/s10115-017-1140-3es_ES
dc.rightsTodos os dereitos reservados. All rights reserved.es_ES
dc.rightsCopyright © 2017, Springer-Verlag London Ltd., part of Springer Naturees_ES
dc.subjectFeature selectiones_ES
dc.subjectScalabilityes_ES
dc.subjectBig dataes_ES
dc.subjectHigh-dimensionalityes_ES
dc.titleOn the scalability of feature selection methods on high-dimensional dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleKnowledge and Information Systemses_ES
UDC.volume56es_ES
UDC.startPage395es_ES
UDC.endPage442es_ES
dc.identifier.doi10.1007/s10115-017-1140-3


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