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dc.contributor.authorKordos, Mirosław
dc.contributor.authorArnaiz González, Álvar 
dc.contributor.authorGarcía Osorio, César 
dc.date.accessioned2019-06-07T11:14:53Z
dc.date.available2019-06-07T11:14:53Z
dc.date.issued2019
dc.identifier.issn0925-2312
dc.identifier.urihttp://hdl.handle.net/10259/5114
dc.description.abstractA novel approach to prototype selection for multi-output regression data sets is presented. A multi-objective evolutionary algorithm is used to evaluate the selections using two criteria: training data set compression and prediction quality expressed in terms of root mean squared error. A multi-target regressor based on k-NN was used for that purpose during the training to evaluate the error, while the tests were performed using four different multi-target predictive models. The distance matrices used by the multi-target regressor were cached to accelerate operational performance. Multiple Pareto fronts were also used to prevent overfitting and to obtain a broader range of solutions, by using different probabilities in the initialization of populations and different evolutionary parameters in each one. The results obtained with the benchmark data sets showed that the proposed method greatly reduced data set size and, at the same time, improved the predictive capabilities of the multi-output regressors trained on the reduced data set.en
dc.description.sponsorshipNCN (Polish National Science Center) grant “Evolutionary Methods in Data Selection” No. 2017/01/X/ST6/00202, project TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, and project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León cofinanced with European Union FEDER funds.en
dc.format.mimetypeapplication/pdf
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofNeurocomputing. 2019, V. 358, p. 309-320en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPrototype selectionen
dc.subjectMulti-outputen
dc.subjectMulti-targeten
dc.subjectRegressionen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleEvolutionary prototype selection for multi-output regressionen
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttps://doi.org/10.1016/j.neucom.2019.05.055
dc.identifier.doi10.1016/j.neucom.2019.05.055
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/TIN2015-67534-P
dc.relation.projectIDinfo:eu-repo/grantAgreement/JCyL/BU085P17
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion


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