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dc.creatorTallón Ballesteros, Antonio Javieres
dc.creatorHervás Martínez, Césares
dc.creatorRiquelme Santos, José Cristóbales
dc.creatorRuiz, Robertoes
dc.date.accessioned2016-07-13T08:15:44Z
dc.date.available2016-07-13T08:15:44Z
dc.date.issued2013
dc.identifier.citationTallón Ballesteros, A.J., Hervás Martínez, C., Riquelme Santos, J.C. y Ruíz, R. (2013). Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems. Neurocomputing, 114, 107-117.
dc.identifier.issn0925-2312es
dc.identifier.urihttp://hdl.handle.net/11441/43538
dc.description.abstractThis paper combines feature selection methods with a two-stage evolutionary classifier based on product unit neural networks. The enhanced methodology has been tried out with four filters using 18 data sets that report test error rates about 20 % or above with reference classifiers such as C4.5 or 1-NN. The proposal has also been evaluated in a liver-transplantation real-world problem with serious troubles in the data distribution and classifiers get low performance. The study includes an overall empirical comparison between the models obtained with and without feature selection using different kind of neural networks, like RBF, MLP and other state-of-the-art classifiers. Statistical tests show that our proposal significantly improves the test accuracy of the previous models. The reduction percentage in the number of inputs is, on average, above 55 %, thus a greater efficiency is achieved.es
dc.description.sponsorshipMICYT TIN2007-68084- C02-02es
dc.description.sponsorshipMICYT TIN2008-06681-C06-03es
dc.description.sponsorshipMICYT TIN2011-28956-C02es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofNeurocomputing, 114, 107-117.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectArtificial neural networkses
dc.subjectProduct unitses
dc.subjectevolutionary algorithmses
dc.subjectclassificationes
dc.subjectFeature selectiones
dc.subjectHigh error problemses
dc.titleFeature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problemses
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDTIN2007-68084- C02-02es
dc.relation.projectIDTIN2008-06681-C06-03es
dc.relation.projectIDTIN2011-28956-C02es
dc.identifier.doihttp://dx.doi.org/10.1016/j.neucom.2012.08.041es
idus.format.extent11es
dc.journaltitleNeurocomputinges
dc.publication.volumen114es
dc.publication.initialPage107es
dc.publication.endPage117es
dc.identifier.idushttps://idus.us.es/xmlui/handle/11441/43538

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