Multi-objective neural network model selection with a graph-based large margin approach

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dc.creatorLuiz Carlos Bambirra Torres
dc.creatorCristiano Leite de Castro
dc.creatorHonovan Paz Rocha
dc.creatorGustavo Matheus de Almeida
dc.creatorAntônio de Pádua Braga
dc.date.accessioned2025-06-04T14:07:28Z
dc.date.accessioned2025-09-08T23:10:52Z
dc.date.available2025-06-04T14:07:28Z
dc.date.issued2022
dc.identifier.doihttps://doi.org/10.1016/j.ins.2022.03.019
dc.identifier.issn00200255
dc.identifier.urihttps://hdl.handle.net/1843/82774
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInformation Sciences
dc.rightsAcesso Restrito
dc.subjectRedes neurais (Computação)
dc.subject.otherClassification, Decision-making, Artificial neural networks, Multi-objective decision learning
dc.titleMulti-objective neural network model selection with a graph-based large margin approach
dc.typeArtigo de periódico
local.citation.epage207
local.citation.spage192
local.citation.volume599
local.description.resumoThis work presents a new decision-making strategy for multi-objective learning problem of artificial neural networks (ANN). The proposed decision-maker searches for the solution that minimizes a margin-based validation error amongst Pareto set solutions. The proposal is based on a geometric approximation to find the large margin (distance) of separation among the classes. Several benchmarks commonly available in the literature were used for testing. The obtained results showed that the proposal is more efficient in controlling the generalization capacity of neural models than other learning machines. It yields smooth (noise robustness) and well-fitted models straightforwardly, i.e., without the necessity of parameter set definition in advance or validation data use, as often required by learning machines.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA QUÍMICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S0020025522002195

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