Modern metaheuristic with multi-objective formulation for the variable selection problem

Página do item simplificado
dc.creatorPaula, Lauro Cássio Martins de
dc.creatorSoares, Anderson da Silva
dc.creatorSoares, Telma Woerle de Lima
dc.creatorCoelho, Clarimar José
dc.creatorOliveira, Anselmo Elcana de
dc.date.accessioned2023-06-19T11:30:43Z
dc.date.available2023-06-19T11:30:43Z
dc.date.issued2017
dc.description.abstractThe development of efficient algorithms for variable selection becomes important to deal with large and complex datasets. Most works in quantitative chemical analysis have used Genetic Algorithms (GAs) as a reference method to select variables. On the other hand, new advances in metaheuristic techniques provide novel possibilities in this task Moreover, the application of Multi-Objective Optimization (MOO) may significantly contribute to efficiently construct an accurate model in the context of multivariate calibration. MOO has showed itself as an efficiently and successful tool to dealing with conflicting objective-functions. For instance, the use of MOO may be considered as a good choice to treat the reducing of prediction error and the number of selected variables in a calibration model. In this paper, we present a modern metaheuristic implementation called Multi-Objective Firefly Algorithm (MOFA) for variable selection in multivariate calibration models. The goal is to propose an optimization to reduce the prediction error of the property of interest in the analysed sample as well as reducing the number of selected variables. However, the outcomes are remarkably promising compared with the previous work. Based on the results obtained, it is possible to demonstrate that our proposal is a viable alternative in order to deal with such conflicting objectives. Additionally, we compare MOFA with a traditional GA implementation and show that MOFA is more efficient for the variable selection problem.pt_BR
dc.identifier.citationPAULA, Lauro Cassio Martins de et al. Modern metaheuristic with multi-objective formulation for the variable selection problem. Journal of Computer Science, Dubai, v. 13, n. 11, p. 659-666, 2017. DOI: 10.3844/jcssp.2017.659.666. Disponível em: https://thescipub.com/abstract/10.3844/jcssp.2017.659.666. Acesso em: 14 jun. 2023.pt_BR
dc.identifier.doihttps://doi.org/10.3844/jcssp.2017.659.666
dc.identifier.issne- 1552-6607
dc.identifier.issn1549-3636
dc.identifier.urihttp://repositorio.bc.ufg.br/handle/ri/22745
dc.language.isoengpt_BR
dc.publisher.countryOutrospt_BR
dc.publisher.departmentInstituto de Química - IQ (RMG)pt_BR
dc.rightsAcesso Abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectVariable selectionpt_BR
dc.subjectMultivariate calibrationpt_BR
dc.subjectFirefly algorithmpt_BR
dc.titleModern metaheuristic with multi-objective formulation for the variable selection problempt_BR
dc.typeArtigopt_BR

Arquivos

Pacote Original
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
Artigo - Lauro Cássio Martins de Paula - 2017.pdf
Tamanho:
218.18 KB
Formato:
Adobe Portable Document Format
Descrição:
Baixar
Licença do Pacote
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Baixar

Coleções

IQ - Artigos publicados em periódicos