Multi-objective decision in machine learning.

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dc.contributor.authorMedeiros, Talles Henrique de
dc.contributor.authorRocha, Honovan Paz
dc.contributor.authorTorres, Frank Sill
dc.contributor.authorTakahashi, Ricardo Hiroshi Caldeira
dc.contributor.authorBraga, Antônio de Pádua
dc.date.accessioned2018-01-18T13:41:07Z
dc.date.available2018-01-18T13:41:07Z
dc.date.issued2016
dc.description.abstractThiswork presents a novel approach for decisionmaking for multi-objective binary classification problems. The purpose of the decision process is to select within a set of Pareto-optimal solutions, one model that minimizes the structural risk (generalization error). This new approach utilizes a kind of prior knowledge that, if available, allows the selection of a model that better represents the problem in question. Prior knowledge about the imprecisions of the collected data enables the identification of the region of equivalent solutions within the set of Pareto-optimal solutions. Results for binary classification problems with sets of synthetic and real data indicate equal or better performance in terms of decision efficiency compared to similar approaches.pt_BR
dc.identifier.citationMEDEIROS, T. H. de et al. Multi-objective decision in machine learning. Journal of Control, Automation and Electrical Systems, v. 4, p. 217–227, 2016. Disponível em: <https://link.springer.com/article/10.1007/s40313-016-0295-6>. Acesso em: 02 out. 2017.pt_BR
dc.identifier.doihttps://doi.org/10.1007/s40313-016-0295-6
dc.identifier.issn 2195-3899
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/9271
dc.identifier.uri2https://link.springer.com/article/10.1007/s40313-016-0295-6pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectMachine learningpt_BR
dc.subjectMulti-objective optimizationpt_BR
dc.subjectDecision-makingpt_BR
dc.subjectClassificationpt_BR
dc.titleMulti-objective decision in machine learning.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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