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dc.contributor.authorJuez Gil, Mario
dc.contributor.authorArnaiz González, Álvar 
dc.contributor.authorRodríguez Diez, Juan José 
dc.contributor.authorLópez Nozal, Carlos 
dc.contributor.authorGarcía Osorio, César 
dc.date.accessioned2021-11-23T08:27:57Z
dc.date.available2021-11-23T08:27:57Z
dc.date.issued2021-10
dc.identifier.issn1566-2535
dc.identifier.urihttp://hdl.handle.net/10259/6207
dc.description.abstractThe Rotation Forest classifier is a successful ensemble method for a wide variety of data mining applications. However, the way in which Rotation Forest transforms the feature space through PCA, although powerful, penalizes training and prediction times, making it unfeasible for Big Data. In this paper, a MapReduce Rotation Forest and its implementation under the Spark framework are presented. The proposed MapReduce Rotation Forest behaves in the same way as the standard Rotation Forest, training the base classifiers on a rotated space, but using a functional implementation of the rotation that enables its execution in Big Data frameworks. Experimental results are obtained using different cloud-based cluster configurations. Bayesian tests are used to validate the method against two ensembles for Big Data: Random Forest and PCARDE classifiers. Our proposal incorporates the parallelization of both the PCA calculation and the tree training, providing a scalable solution that retains the performance of the original Rotation Forest and achieves a competitive execution time (in average, at training, more than 3 times faster than other PCA-based alternatives). In addition, extensive experimentation shows that by setting some parameters of the classifier (i.e., bootstrap sample size, number of trees, and number of rotations), the execution time is reduced with no significant loss of performance using a small ensemble.en
dc.description.sponsorshipThis work was supported through project TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, projects BU085P17 and BU055P20 (JCyL/FEDER, UE) of the Junta de Castilla y León, Spain (both projects co-financed through European Union FEDER funds), and by the Consejería de Educación of the Junta de Castilla y León, Spain and the European Social Fund through a pre-doctoral grant (EDU/1100/2017). The project leading to these results has received also funding from “la Caixa” Foundation, Spain , under agreement LCF/PR/PR18/51130007. This material is based upon work supported by Google Cloud.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofInformation Fusion. 2021, V. 74, p. 39-49en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRotation Foresten
dc.subjectRandom Foresten
dc.subjectEnsemble learningen
dc.subjectMachine learningen
dc.subjectBig Dataen
dc.subjectSparken
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleRotation Forest for Big Dataen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1016/j.inffus.2021.03.007es
dc.identifier.doi10.1016/j.inffus.2021.03.007
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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