Multi-objective decision in machine learning.

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorMedeiros, Talles Henrique de-
Autor(es): dc.creatorRocha, Honovan Paz-
Autor(es): dc.creatorTorres, Frank Sill-
Autor(es): dc.creatorTakahashi, Ricardo Hiroshi Caldeira-
Autor(es): dc.creatorBraga, Antônio de Pádua-
Data de aceite: dc.date.accessioned2022-02-21T19:58:53Z-
Data de disponibilização: dc.date.available2022-02-21T19:58:53Z-
Data de envio: dc.date.issued2018-01-18-
Data de envio: dc.date.issued2018-01-18-
Data de envio: dc.date.issued2016-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/9271-
Fonte completa do material: dc.identifierhttps://link.springer.com/article/10.1007/s40313-016-0295-6-
Fonte completa do material: dc.identifierhttps://doi.org/10.1007/s40313-016-0295-6-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/650627-
Descrição: dc.descriptionThiswork 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.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectMulti-objective optimization-
Palavras-chave: dc.subjectDecision-making-
Palavras-chave: dc.subjectClassification-
Título: dc.titleMulti-objective decision in machine learning.-
Aparece nas coleções:Repositório Institucional - UFOP

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