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dc.contributor.authorBaíllo Moreno, Amparo 
dc.contributor.authorGrané, Aurea
dc.contributor.otherUAM. Departamento de Matemáticases_ES
dc.date.accessioned2022-11-23T13:02:14Z
dc.date.available2022-11-23T13:02:14Z
dc.date.issued2021-09-13
dc.identifier.citationMathematics 9.18 (2021): 2247es_ES
dc.identifier.issn2227-7390 (online)es_ES
dc.identifier.urihttp://hdl.handle.net/10486/705323
dc.description.abstractThe distance-based linear model (DB-LM) extends the classical linear regression to the framework of mixed-type predictors or when the only available information is a distance matrix between regressors (as it sometimes happens with big data). The main drawback of these DB methods is their computational cost, particularly due to the eigendecomposition of the Gram matrix. In this context, ensemble regression techniques provide a useful alternative to fitting the model to the whole sample. This work analyzes the performance of three subsampling and aggregation techniques in DB regression on two specific large, real datasets. We also analyze, via simulations, the performance of bagging and DB logistic regression in the classification problem with mixed-type features and large sample sizeses_ES
dc.format.extent17 pag.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.ispartofMathematicses_ES
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerlandes_ES
dc.subject.otherClassificationes_ES
dc.subject.otherDissimilaritieses_ES
dc.subject.otherEnsemblees_ES
dc.subject.otherBig Dataes_ES
dc.subject.otherGeneralized Linear Modeles_ES
dc.subject.otherGower’s Metrices_ES
dc.subject.otherMachine Learninges_ES
dc.titleSubsampling and aggregation: A solution to the scalability problem in distance-based prediction for mixed-type dataes_ES
dc.typearticlees_ES
dc.subject.ecienciaMatemáticases_ES
dc.relation.publisherversionhttps://doi.org/10.3390/math9182247es_ES
dc.identifier.doi10.3390/math9182247es_ES
dc.identifier.publicationfirstpage2247-1es_ES
dc.identifier.publicationissue18es_ES
dc.identifier.publicationlastpage2247-17es_ES
dc.identifier.publicationvolume9es_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.ccReconocimientoes_ES
dc.rights.accessRightsopenAccesses_ES
dc.facultadUAMFacultad de Cienciases_ES


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