Genetic programming for stacked generalization

Detalhes bibliográficos
Autor(a) principal: Bakurov, Illya
Data de Publicação: 2021
Outros Autores: Castelli, Mauro, Gau, Olivier, Fontanella, Francesco, Vanneschi, Leonardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/119932
Resumo: Bakurov, I., Castelli, M., Gau, O., Fontanella, F., & Vanneschi, L. (2021). Genetic programming for stacked generalization. Swarm and Evolutionary Computation, 65, 1-14. [100913]. https://doi.org/10.1016/j.swevo.2021.100913
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spelling Genetic programming for stacked generalizationEnsemble LearningGenetic ProgrammingStacked GeneralizationStackingComputer Science(all)Mathematics(all)Bakurov, I., Castelli, M., Gau, O., Fontanella, F., & Vanneschi, L. (2021). Genetic programming for stacked generalization. Swarm and Evolutionary Computation, 65, 1-14. [100913]. https://doi.org/10.1016/j.swevo.2021.100913In machine learning, ensemble techniques are widely used to improve the performance of both classification and regression systems. They combine the models generated by different learning algorithms, typically trained on different data subsets or with different parameters, to obtain more accurate models. Ensemble strategies range from simple voting rules to more complex and effective stacked approaches. They are based on adopting a meta-learner, i.e. a further learning algorithm, and are trained on the predictions provided by the single algorithms making up the ensemble. The paper aims at exploiting some of the most recent genetic programming advances in the context of stacked generalization. In particular, we investigate how the evolutionary demes despeciation initialization technique, ϵ-lexicase selection, geometric-semantic operators, and semantic stopping criterion, can be effectively used to improve GP-based systems’ performance for stacked generalization (a.k.a. stacking). The experiments, performed on a broad set of synthetic and real-world regression problems, confirm the effectiveness of the proposed approach.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBakurov, IllyaCastelli, MauroGau, OlivierFontanella, FrancescoVanneschi, Leonardo2024-01-24T01:31:45Z2021-082021-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/119932eng2210-6502PURE: 32162340https://doi.org/10.1016/j.swevo.2021.100913info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T17:54:09Zoai:run.unl.pt:10362/119932Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:54:09Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Genetic programming for stacked generalization
title Genetic programming for stacked generalization
spellingShingle Genetic programming for stacked generalization
Bakurov, Illya
Ensemble Learning
Genetic Programming
Stacked Generalization
Stacking
Computer Science(all)
Mathematics(all)
title_short Genetic programming for stacked generalization
title_full Genetic programming for stacked generalization
title_fullStr Genetic programming for stacked generalization
title_full_unstemmed Genetic programming for stacked generalization
title_sort Genetic programming for stacked generalization
author Bakurov, Illya
author_facet Bakurov, Illya
Castelli, Mauro
Gau, Olivier
Fontanella, Francesco
Vanneschi, Leonardo
author_role author
author2 Castelli, Mauro
Gau, Olivier
Fontanella, Francesco
Vanneschi, Leonardo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Bakurov, Illya
Castelli, Mauro
Gau, Olivier
Fontanella, Francesco
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Ensemble Learning
Genetic Programming
Stacked Generalization
Stacking
Computer Science(all)
Mathematics(all)
topic Ensemble Learning
Genetic Programming
Stacked Generalization
Stacking
Computer Science(all)
Mathematics(all)
description Bakurov, I., Castelli, M., Gau, O., Fontanella, F., & Vanneschi, L. (2021). Genetic programming for stacked generalization. Swarm and Evolutionary Computation, 65, 1-14. [100913]. https://doi.org/10.1016/j.swevo.2021.100913
publishDate 2021
dc.date.none.fl_str_mv 2021-08
2021-08-01T00:00:00Z
2024-01-24T01:31:45Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/119932
url http://hdl.handle.net/10362/119932
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2210-6502
PURE: 32162340
https://doi.org/10.1016/j.swevo.2021.100913
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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