Evolutionary design of neural networks for classification and regression

Bibliographic Details
Main Author: Rocha, Miguel
Publication Date: 2005
Other Authors: Cortez, Paulo, Neves, José
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/1822/892
Summary: Comunicação aprovada à ICANGA March 2005, Coimbra.
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spelling Evolutionary design of neural networks for classification and regressionSupervised machine learningMultilayer perceptronsEvolutionary algorithmsEnsemblesScience & TechnologyComunicação aprovada à ICANGA March 2005, Coimbra.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.Fundação para a Ciência e Tecnologia - Project POSI/ROBO/43904/2002; FEDER.SpringerUniversidade do MinhoRocha, MiguelCortez, PauloNeves, José2005-032005-03-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/892engRIBEIRO, 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. 304-307.3211249346The original publication is available at http://www.springerlink.cominfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T05:29:24Zoai:repositorium.sdum.uminho.pt:1822/892Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:20:04.485921Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Evolutionary design of neural networks for classification and regression
title Evolutionary design of neural networks for classification and regression
spellingShingle Evolutionary design of neural networks for classification and regression
Rocha, Miguel
Supervised machine learning
Multilayer perceptrons
Evolutionary algorithms
Ensembles
Science & Technology
title_short Evolutionary design of neural networks for classification and regression
title_full Evolutionary design of neural networks for classification and regression
title_fullStr Evolutionary design of neural networks for classification and regression
title_full_unstemmed Evolutionary design of neural networks for classification and regression
title_sort Evolutionary design of neural networks for classification and regression
author Rocha, Miguel
author_facet Rocha, Miguel
Cortez, Paulo
Neves, José
author_role author
author2 Cortez, Paulo
Neves, José
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Rocha, Miguel
Cortez, Paulo
Neves, José
dc.subject.por.fl_str_mv Supervised machine learning
Multilayer perceptrons
Evolutionary algorithms
Ensembles
Science & Technology
topic Supervised machine learning
Multilayer perceptrons
Evolutionary algorithms
Ensembles
Science & Technology
description Comunicação aprovada à ICANGA March 2005, Coimbra.
publishDate 2005
dc.date.none.fl_str_mv 2005-03
2005-03-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/892
url http://hdl.handle.net/1822/892
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv RIBEIRO, 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.
3211249346
The original publication is available at http://www.springerlink.com
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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