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dc.contributor.authorRodríguez Diez, Juan José 
dc.contributor.authorJuez Gil, Mario 
dc.contributor.authorLópez Nozal, Carlos 
dc.contributor.authorArnaiz González, Álvar 
dc.date.accessioned2022-05-11T11:19:35Z
dc.date.available2022-05-11T11:19:35Z
dc.date.issued2022-02
dc.identifier.issn1868-8071
dc.identifier.urihttp://hdl.handle.net/10259/6645
dc.description.abstractThe prediction of multiple numeric outputs at the same time is called multi-target regression (MTR), and it has gained attention during the last decades. This task is a challenging research topic in supervised learning because it poses additional difficulties to traditional single-target regression (STR), and many real-world problems involve the prediction of multiple targets at once. One of the most successful approaches to deal with MTR, although not the only one, consists in transforming the problem in several STR problems, whose outputs will be combined building up the MTR output. In this paper, the Rotation Forest ensemble method, previously proposed for single-label classification and single-target regression, is adapted to MTR tasks and tested with several regressors and data sets. Our proposal rotates the input space in an efficient and novel fashion, avoiding extra rotations forced by MTR problem decomposition. Four approaches for MTR are used: single-target (ST), stacked-single target (SST), Ensembles of Regressor Chains (ERC), and Multi-target Regression via Quantization (MRQ). For assessing the benefits of the proposal, a thorough experimentation with 28 MTR data sets and statistical tests are used, concluding that Rotation Forest, adapted by means of these approaches, outperforms other popular ensembles, such as Bagging and Random Forest.es
dc.description.sponsorshipMinisterio de Economía y Competitividad of the Spanish Government under project TIN2015-67534-P (MINECO-FEDER, UE), by the Junta de Castilla y León under project BU085P17 (JCyL/FEDER, UE) (both projects co-financed through European Union FEDER funds), and by the Consejería de Educación of the Junta de Castilla y León and the European Social Fund with the EDU/1100/2017 pre-doctoral grant.es
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofInternational Journal of Machine Learning and Cybernetics. 2022, V. 13, n. 2, p. 523-548es
dc.subjectMulti-target regressiones
dc.subjectEnsemblees
dc.subjectRotation Forestes
dc.subject.otherInformáticaes
dc.subject.otherComputer sciencees
dc.titleRotation Forest for multi-target regressiones
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1007/s13042-021-01329-1es
dc.identifier.doi10.1007/s13042-021-01329-1
dc.identifier.essn1868-808X
dc.journal.titleInternational Journal of Machine Learning and Cyberneticses
dc.volume.number13es
dc.issue.number2es
dc.page.initial523es
dc.page.final548es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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