Pruning techniques for mixed ensembles of genetic programming models

Detalhes bibliográficos
Autor(a) principal: Castelli, Mauro
Data de Publicação: 2018
Outros Autores: Gonçalves, Ivo, Manzoni, Luca, Vanneschi, Leonardo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/146339
Resumo: Castelli, M., Gonçalves, I., Manzoni, L., & Vanneschi, L. (2018). Pruning techniques for mixed ensembles of genetic programming models. In M. Castelli, L. Sekanina, M. Zhang, S. Cagnoni, & P. García-Sánchez (Eds.), Genetic Programming : 21st European Conference, EuroGP 2018, Proceedings (pp. 52-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10781 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-77553-1_4
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spelling Pruning techniques for mixed ensembles of genetic programming modelsTheoretical Computer ScienceComputer Science(all)Castelli, M., Gonçalves, I., Manzoni, L., & Vanneschi, L. (2018). Pruning techniques for mixed ensembles of genetic programming models. In M. Castelli, L. Sekanina, M. Zhang, S. Cagnoni, & P. García-Sánchez (Eds.), Genetic Programming : 21st European Conference, EuroGP 2018, Proceedings (pp. 52-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10781 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-77553-1_4The objective of this paper is to define an effective strategy for building an ensemble of Genetic Programming (GP) models. Ensemble methods are widely used in machine learning due to their features: they average out biases, they reduce the variance and they usually generalize better than single models. Despite these advantages, building ensemble of GP models is not a well-developed topic in the evolutionary computation community. To fill this gap, we propose a strategy that blends individuals produced by standard syntax-based GP and individuals produced by geometric semantic genetic programming, one of the newest semantics-based method developed in GP. In fact, recent literature showed that combining syntax and semantics could improve the generalization ability of a GP model. Additionally, to improve the diversity of the GP models used to build up the ensemble, we propose different pruning criteria that are based on correlation and entropy, a commonly used measure in information theory. Experimental results, obtained over different complex problems, suggest that the pruning criteria based on correlation and entropy could be effective in improving the generalization ability of the ensemble model and in reducing the computational burden required to build it.Springer VerlagInformation Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNCastelli, MauroGonçalves, IvoManzoni, LucaVanneschi, Leonardo2022-12-16T22:18:18Z2018-01-012018-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion16application/pdfhttp://hdl.handle.net/10362/146339eng97833197755240302-9743PURE: 3938895https://doi.org/10.1007/978-3-319-77553-1_4info: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:RCAAP2024-05-22T18:07:28Zoai:run.unl.pt:10362/146339Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:38:08.412520Repositórios 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 Pruning techniques for mixed ensembles of genetic programming models
title Pruning techniques for mixed ensembles of genetic programming models
spellingShingle Pruning techniques for mixed ensembles of genetic programming models
Castelli, Mauro
Theoretical Computer Science
Computer Science(all)
title_short Pruning techniques for mixed ensembles of genetic programming models
title_full Pruning techniques for mixed ensembles of genetic programming models
title_fullStr Pruning techniques for mixed ensembles of genetic programming models
title_full_unstemmed Pruning techniques for mixed ensembles of genetic programming models
title_sort Pruning techniques for mixed ensembles of genetic programming models
author Castelli, Mauro
author_facet Castelli, Mauro
Gonçalves, Ivo
Manzoni, Luca
Vanneschi, Leonardo
author_role author
author2 Gonçalves, Ivo
Manzoni, Luca
Vanneschi, Leonardo
author2_role author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Castelli, Mauro
Gonçalves, Ivo
Manzoni, Luca
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Theoretical Computer Science
Computer Science(all)
topic Theoretical Computer Science
Computer Science(all)
description Castelli, M., Gonçalves, I., Manzoni, L., & Vanneschi, L. (2018). Pruning techniques for mixed ensembles of genetic programming models. In M. Castelli, L. Sekanina, M. Zhang, S. Cagnoni, & P. García-Sánchez (Eds.), Genetic Programming : 21st European Conference, EuroGP 2018, Proceedings (pp. 52-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10781 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-77553-1_4
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
2018-01-01T00:00:00Z
2022-12-16T22:18:18Z
dc.type.driver.fl_str_mv conference object
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 9783319775524
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PURE: 3938895
https://doi.org/10.1007/978-3-319-77553-1_4
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