Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/892

TitleEvolutionary design of neural networks for classification and regression
Author(s)Rocha, Miguel
Cortez, Paulo
Neves, José
KeywordsSupervised machine learning
Multilayer perceptrons
Evolutionary algorithms
Ensembles
Issue dateMar-2005
PublisherSpringer
CitationRIBEIRO, B.; ALBRECHT, R.; DOBNIKAR, D., ed. lit. – “Adaptive and natural computing algorithms : proceedings of ICANGA, Coimbra, 2005.”. Springer: New York, 2005. ISBN 3-211-24934-6. p. 304-307.
Abstract(s)The Multilayer Perceptrons (MLPs) are the most popular class of Neural Networks. When applying MLPs, the search for the ideal architecture is a crucial task, since it should should be complex enough to learn the input/output mapping, without overfitting the training data. Under this context, the use of Evolutionary Computation makes a promising global search approach for model selection. On the other hand, ensembles (combinations of models) have been boosting the performance of several Machine Learning (ML) algorithms. In this work, a novel evolutionary technique for MLP design is presented, being also used an ensemble based approach. A set of real world classification and regression tasks was used to test this strategy, comparing it with a heuristic model selection, as well as with other ML algorithms. The results favour the evolutionary MLP ensemble method.
TypeconferencePaper
DescriptionComunicação aprovada à ICANGA March 2005, Coimbra.
URIhttp://hdl.handle.net/1822/892
ISBN3211249346
Publisher versionThe original publication is available at http://www.springerlink.com
Peer-Reviewedyes
AccessopenAccess
Appears in Collections:DSI - Engenharia da Programação e dos Sistemas Informáticos
DI/CCTC - Artigos (papers)
CAlg - Artigos em livros de atas/Papers in proceedings

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