Publicação: Pruning methods to MLP neural networks considering proportional apparent error rate for classification problems with unbalanced data
Carregando...
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Elsevier B.V.
Tipo
Artigo
Direito de acesso
Acesso restrito
Resumo
This article deals with classification problems involving unequal probabilities in each class and discusses metrics to systems that use multilayer perceptrons neural networks (MLP) for the task of classifying new patterns. In addition we propose three new pruning methods that were compared to other seven existing methods in the literature for MLP networks. All pruning algorithms presented in this paper have been modified by the authors to do pruning of neurons, in order to produce fully connected MLP networks but being small in its intermediary layer. Experiments were carried out involving the E. coli unbalanced classification problem and ten pruning methods. The proposed methods had obtained good results, actually, better results than another pruning methods previously defined at the MLP neural network area. (C) 2014 Elsevier Ltd. All rights reserved.
Descrição
Palavras-chave
Unbalanced data, Pruning method, MLP neural network, Proportional apparent error rate
Idioma
Inglês
Como citar
Measurement. Oxford: Elsevier Sci Ltd, v. 56, p. 88-94, 2014.
