Pruning Optimum-Path Forest Classifiers Using Multi-Objective Optimization
Carregando...
Arquivos
Fontes externas
Fontes externas
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Ieee
Tipo
Trabalho apresentado em evento
Direito de acesso
Acesso aberto
Arquivos
Fontes externas
Fontes externas
Resumo
Multi-objective optimization plays an important role when one has fitness functions that are somehow conflicting with each other. Also, parameter-dependent machine learning techniques can benefit from such optimization tools. In this paper, we propose a multi-objective-based strategy approach to build compact though representative training sets for Optimum-Path Forest (OPF) learning purposes. Although OPF pruning can provide such a nice representation, it comes with the price of being parameter-dependent. The proposed approach cope with that problem by avoiding the classifier to be hand-tuned by modeling the task of parameter learning as a multi-objective-oriented optimization problem, which can be less prone to errors. Experiments on public datasets show the robustness of the proposed approach, which is now parameterless and userfriendly.
Descrição
Palavras-chave
Idioma
Inglês
Citação
2017 30th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 127-133, 2017.
