Exploring SLUG

Detalhes bibliográficos
Autor(a) principal: Rodrigues, Nuno M.
Data de Publicação: 2023
Outros Autores: Batista, João E., La Cava, William, Vanneschi, Leonardo, Silva, Sara
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/161429
Resumo: Rodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., & Silva, S. (2024). Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. SN Computer Science, 5(1), 1-17. [91]. https://doi.org/10.1007/s42979-023-02106-3 --- Open access funding provided by FCT|FCCN (b-on). This work was partially supported by the FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020); MAR2020 program via project MarCODE (MAR$$-$$01.03.01-FEAMP-0047); project AICE (DSAIPA/DS/0113/2019). Nuno Rodrigues and João Batista were supported by PhD Grants 2021/05322/BD and SFRH/BD/143972/2019, respectively; William La Cava was supported by the National Library Of Medicine of the National Institutes of Health under Award Number R00LM012926
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spelling Exploring SLUGFeature Selection Using Genetic Algorithms and Genetic ProgrammingFeature selectionEpistasisGenetic ProgrammingGenetic algorithmsWrapperLearnerMachine learningComputer Science(all)Computer Science ApplicationsComputer Networks and CommunicationsComputer Graphics and Computer-Aided DesignComputational Theory and MathematicsArtificial IntelligenceRodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., & Silva, S. (2024). Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. SN Computer Science, 5(1), 1-17. [91]. https://doi.org/10.1007/s42979-023-02106-3 --- Open access funding provided by FCT|FCCN (b-on). This work was partially supported by the FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020); MAR2020 program via project MarCODE (MAR$$-$$01.03.01-FEAMP-0047); project AICE (DSAIPA/DS/0113/2019). Nuno Rodrigues and João Batista were supported by PhD Grants 2021/05322/BD and SFRH/BD/143972/2019, respectively; William La Cava was supported by the National Library Of Medicine of the National Institutes of Health under Award Number R00LM012926We present SLUG, a recent method that uses genetic algorithms as a wrapper for genetic programming and performs feature selection while inducing models. SLUG was shown to be successful on different types of classification tasks, achieving state-of-the-art results on the synthetic datasets produced by GAMETES, a tool for embedding epistatic gene–gene interactions into noisy datasets. SLUG has also been studied and modified to demonstrate that its two elements, wrapper and learner, are the right combination that grants it success. We report these results and test SLUG on an additional six GAMETES datasets of increased difficulty, for a total of four regular and 16 epistatic datasets. Despite its slowness, SLUG achieves the best results and solves all but the most difficult classification tasks. We perform further explorations of its inner dynamics and discover how to improve the feature selection by enriching the communication between wrapper and learner, thus taking the first step toward a new and more powerful SLUG.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNRodrigues, Nuno M.Batista, João E.La Cava, WilliamVanneschi, LeonardoSilva, Sara2023-12-18T22:38:32Z2024-012024-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttp://hdl.handle.net/10362/161429eng2662-995XPURE: 78657697https://doi.org/10.1007/s42979-023-02106-3info: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:RCAAP2024-03-11T05:44:19Zoai:run.unl.pt:10362/161429Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:58:31.382492Repositório 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 Exploring SLUG
Feature Selection Using Genetic Algorithms and Genetic Programming
title Exploring SLUG
spellingShingle Exploring SLUG
Rodrigues, Nuno M.
Feature selection
Epistasis
Genetic Programming
Genetic algorithms
Wrapper
Learner
Machine learning
Computer Science(all)
Computer Science Applications
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Computational Theory and Mathematics
Artificial Intelligence
title_short Exploring SLUG
title_full Exploring SLUG
title_fullStr Exploring SLUG
title_full_unstemmed Exploring SLUG
title_sort Exploring SLUG
author Rodrigues, Nuno M.
author_facet Rodrigues, Nuno M.
Batista, João E.
La Cava, William
Vanneschi, Leonardo
Silva, Sara
author_role author
author2 Batista, João E.
La Cava, William
Vanneschi, Leonardo
Silva, Sara
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 Rodrigues, Nuno M.
Batista, João E.
La Cava, William
Vanneschi, Leonardo
Silva, Sara
dc.subject.por.fl_str_mv Feature selection
Epistasis
Genetic Programming
Genetic algorithms
Wrapper
Learner
Machine learning
Computer Science(all)
Computer Science Applications
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Computational Theory and Mathematics
Artificial Intelligence
topic Feature selection
Epistasis
Genetic Programming
Genetic algorithms
Wrapper
Learner
Machine learning
Computer Science(all)
Computer Science Applications
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Computational Theory and Mathematics
Artificial Intelligence
description Rodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., & Silva, S. (2024). Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. SN Computer Science, 5(1), 1-17. [91]. https://doi.org/10.1007/s42979-023-02106-3 --- Open access funding provided by FCT|FCCN (b-on). This work was partially supported by the FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020); MAR2020 program via project MarCODE (MAR$$-$$01.03.01-FEAMP-0047); project AICE (DSAIPA/DS/0113/2019). Nuno Rodrigues and João Batista were supported by PhD Grants 2021/05322/BD and SFRH/BD/143972/2019, respectively; William La Cava was supported by the National Library Of Medicine of the National Institutes of Health under Award Number R00LM012926
publishDate 2023
dc.date.none.fl_str_mv 2023-12-18T22:38:32Z
2024-01
2024-01-01T00:00:00Z
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/10362/161429
url http://hdl.handle.net/10362/161429
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2662-995X
PURE: 78657697
https://doi.org/10.1007/s42979-023-02106-3
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 17
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
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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
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