Empirical evaluation of optimized stacking configurations

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dc.contributor.author Ledezma Espino, Agapito Ismael
dc.contributor.author Aler, Ricardo
dc.contributor.author Sanchis de Miguel, María Araceli
dc.contributor.author Borrajo Millán, Daniel
dc.date.accessioned 2009-12-21T11:06:47Z
dc.date.available 2009-12-21T11:06:47Z
dc.date.issued 2004-11
dc.identifier.bibliographicCitation 16th IEEE International Conference on Tools with Artificial Intelligence, 2004, p. 49-55
dc.identifier.isbn 0-7695-2236-X
dc.identifier.issn 1082-3409
dc.identifier.uri http://hdl.handle.net/10016/6188
dc.description Proceeding of: 16th IEEE International Conference on Tools with Artificial Intelligence, 15-17 Nov. 2004, Boca Ratón, Florida
dc.description.abstract Stacking 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.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © IEEE
dc.subject.other Data structures
dc.subject.other Genetic algorithms
dc.subject.other Learning (artificial intelligence)
dc.subject.other Pattern classification
dc.subject.other Search problems
dc.title Empirical evaluation of optimized stacking configurations
dc.type bookPart
dc.type conferenceObject
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1109/ICTAI.2004.56
dc.subject.eciencia Informática
dc.identifier.doi 10.1109/ICTAI.2004.56
dc.rights.accessRights openAccess
dc.relation.eventdate 15-17 Nov
dc.relation.eventnumber 16
dc.relation.eventplace Boca Ratón (Florida, USA)
dc.relation.eventtitle IEEE International Conference on Tools with Artificial Intelligence 2004. ICTAI 2004
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 49
dc.identifier.publicationlastpage 55
dc.identifier.publicationtitle 16th IEEE International Conference on Tools with Artificial Intelligence 2004. ICTAI 2004
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