Publicação:
Improving Accuracy and Speed of Optimum-Path Forest Classifier Using Combination of Disjoint Training Subsets

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 Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OFF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure, The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OFF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more.

Descrição

Idioma

Inglês

Como citar

Multiple Classifier Systems. Berlin: Springer-verlag Berlin, v. 6713, p. 237-+, 2011.

Itens relacionados

Financiadores

Unidades

Departamentos

Cursos de graduação

Programas de pós-graduação