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Genetic approach for optimizing ensembles of classifiers

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Control, Aprendizaje y Optimización de Sistemas (CAOS)es
dc.contributor.authorOrdóñez Morales, Francisco Javier
dc.contributor.authorLedezma Espino, Agapito Ismael
dc.contributor.authorSanchis de Miguel, María Araceli
dc.date.accessioned2011-02-03T12:28:58Z
dc.date.available2011-02-03T12:28:58Z
dc.date.issued2008
dc.descriptionProceeding of: Twenty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS), Coconut Grove, Florida. May 15–17, 2008
dc.description.abstractAn ensemble of classifiers is a set of classifiers whose predictions are combined in some way to classify new instances. Early research has shown that, in general, an ensemble of classifiers is more accurate than any of the single classifiers in the ensemble. Usually the gains obtained by combining different classifiers are more affected by the chosen classifiers than by the used combination. It is common in the research on this topic to select by hand the right combination of classifiers and the method to combine them, but the approach presented in this work uses genetic algorithms for selecting the classifiers and the combination method to use. Our approach, GA-Ensemble, is inspired by a previous work, called GA-Stacking. GA-Stacking is a method that uses genetic algorithms to find domain-specific Stacking configurations. The main goal of this work is to improve the efficiency of GAStacking and to compare GA-Ensemble with current ensemble building techniques. Preliminary results have show that the approach finds ensembles of classifiers whose performance is as good as the best techniques, without having to set up manually the classifiers and the ensemble method.
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationProceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS), 2008, p. 89-94.
dc.identifier.isbn978-1-57735-365-2 (Print)
dc.identifier.isbn978-1-57735-366-9 (CD-ROM)
dc.identifier.publicationfirstpage89
dc.identifier.publicationlastpage94
dc.identifier.publicationtitleProceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS)
dc.identifier.urihttps://hdl.handle.net/10016/10170
dc.language.isoeng
dc.publisherThe AAAI Press
dc.relation.eventdateMay 15–17, 2008
dc.relation.eventnumber21
dc.relation.eventplaceCoconut Grove (Florida, USA)
dc.relation.eventtitleInternational Florida Artificial Intelligence Research Society Conference (FLAIRS)
dc.relation.publisherversionhttp://www.aaai.org/Papers/FLAIRS/2008/FLAIRS08-026.pdf
dc.rights© Association for the Advancement of Artificial Intelligence (www.aaai.org)
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.titleGenetic approach for optimizing ensembles of classifiers
dc.typebook part*
dc.type.hasVersionAM*
dspace.entity.typePublication
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