Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/9271
Title: Multi-objective decision in machine learning.
Authors: Medeiros, Talles Henrique de
Rocha, Honovan Paz
Torres, Frank Sill
Takahashi, Ricardo Hiroshi Caldeira
Braga, Antônio Pádua
Keywords: Machine learning
Multi-objective optimization
Decision-making
Classification
Issue Date: 2016
Citation: MEDEIROS, 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.
Abstract: Thiswork 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.
URI: http://www.repositorio.ufop.br/handle/123456789/9271
metadata.dc.identifier.uri2: https://link.springer.com/article/10.1007/s40313-016-0295-6
ISSN:  21953899
Appears in Collections:DECSI - Artigos publicados em periódicos

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