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Empirical evaluation of optimized stacking configurations

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Planificación y Aprendizajees
dc.contributor.authorLedezma Espino, Agapito Ismael
dc.contributor.authorAler, Ricardo
dc.contributor.authorSanchis de Miguel, María Araceli
dc.contributor.authorBorrajo Millán, Daniel
dc.date.accessioned2009-12-21T11:06:47Z
dc.date.available2009-12-21T11:06:47Z
dc.date.issued2004-11
dc.descriptionProceeding of: 16th IEEE International Conference on Tools with Artificial Intelligence, 15-17 Nov. 2004, Boca Ratón, Florida
dc.description.abstractStacking is one of the most used techniques for combining classifiers and improves prediction accuracy. Early research in stacking showed that selecting the right classifiers, their parameters and the metaclassifiers was the main bottleneck for its use. Most of the research on this topic selects by hand the right combination of classifiers and their parameters. Instead of starting from these initial strong assumptions, our approach uses genetic algorithms to search for good stacking configurations. Since this can lead to overfitting, one of the goals of This work is to evaluate empirically the overall efficiency of the approach. A second goal is to compare our approach with current best stacking building techniques. The results show that our approach finds stacking configurations that, in the worst case, perform as well as the best techniques, with the advantage of not having to set up manually the structure of the stacking system.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitation16th IEEE International Conference on Tools with Artificial Intelligence, 2004, p. 49-55
dc.identifier.doi10.1109/ICTAI.2004.56
dc.identifier.isbn0-7695-2236-X
dc.identifier.issn1082-3409
dc.identifier.publicationfirstpage49
dc.identifier.publicationlastpage55
dc.identifier.publicationtitle16th IEEE International Conference on Tools with Artificial Intelligence 2004. ICTAI 2004
dc.identifier.urihttps://hdl.handle.net/10016/6188
dc.language.isoeng
dc.publisherIEEE
dc.relation.eventdate15-17 Nov
dc.relation.eventnumber16
dc.relation.eventplaceBoca Ratón (Florida, USA)
dc.relation.eventtitleIEEE International Conference on Tools with Artificial Intelligence 2004. ICTAI 2004
dc.relation.publisherversionhttp://dx.doi.org/10.1109/ICTAI.2004.56
dc.rights© IEEE
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherData structures
dc.subject.otherGenetic algorithms
dc.subject.otherLearning (artificial intelligence)
dc.subject.otherPattern classification
dc.subject.otherSearch problems
dc.titleEmpirical evaluation of optimized stacking configurations
dc.typeconference paper*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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