Pruning Optimum-Path Forest Ensembles Using Quaternion-based Optimization

Carregando...
Imagem de Miniatura

Data

2017-01-01

Título da Revista

ISSN da Revista

Título de Volume

Editor

Ieee

Resumo

Machine learning techniques have been actively pursued in the last years, mainly due to the great number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this context, we shall highlight pruning strategies, which provide heuristics to select from a collection of classifiers the ones that can really improve recognition rates when working together. In this paper, we present an ensemble pruning approach of Optimum-Path Forest classifiers based on metaheuristics, as well as we introduced the concept of quaternions in ensemble pruning strategies. Experimental results over synthetic and real datasets showed the effectiveness and efficiency of the proposed approach for classification problems.

Descrição

Palavras-chave

Como citar

2017 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, p. 984-991, 2017.