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dc.contributor.authorGarcía-Pedrajas, Nicolás
dc.contributor.authorGarcía-Osorio, César
dc.contributor.authorFyfe, Colin
dc.date.accessioned2014-02-27T11:09:54Z
dc.date.available2014-02-27T11:09:54Z
dc.date.issued2007
dc.identifier.urihttp://hdl.handle.net/10396/11871
dc.description.abstractIn this paper we propose a novel approach for ensemble construction based on the use of nonlinear projections to achieve both accuracy and diversity of individual classifiers. The proposed approach combines the philosophy of boosting, putting more effort on difficult instances, with the basis of the random subspace method. Our main contribution is that instead of using a random subspace, we construct a projection taking into account the instances which have posed most difficulties to previous classifiers. In this way, consecutive nonlinear projections are created by a neural network trained using only incorrectly classified instances. The feature subspace induced by the hidden layer of this network is used as the input space to a new classifier. The method is compared with bagging and boosting techniques, showing an improved performance on a large set of 44 problems from the UCI Machine Learning Repository. An additional study showed that the proposed approach is less sensitive to noise in the data than boosting methodses_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherDale Schuurmanses_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceJournal of Machine Learning Research 8, 1-33 (2007)es_ES
dc.subjectClassifier ensembleses_ES
dc.subjectBoostinges_ES
dc.subjectNeural networkses_ES
dc.subjectNonlinear projectionses_ES
dc.titleNonlinear Boosting Projections for Ensemble Constructiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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