A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach
Nenhuma Miniatura disponível
Data
2011-01-01
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Springer
Tipo
Trabalho apresentado em evento
Direito de acesso
Resumo
The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of Off ensembles and the framework to design multiple classifier systems.
Descrição
Palavras-chave
Idioma
Inglês
Como citar
Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications. Berlin: Springer-verlag Berlin, v. 7042, p. 581-+, 2011.
