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Exploring SLUG

2024-01Artigo científico Acesso aberto
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dc.contributor.authorRodrigues, Nuno M.
dc.contributor.authorBatista, João E.
dc.contributor.authorLa Cava, William
dc.contributor.authorVanneschi, Leonardo
dc.contributor.authorSilva, Sara
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblSpringer
dc.date.accessioned2023-12-18T22:38:32Z
dc.date.available2023-12-18T22:38:32Z
dc.date.issued2024-01
dc.descriptionRodrigues, 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
dc.description.abstractWe 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent17
dc.format.extent1267841
dc.identifier.doi10.1007/s42979-023-02106-3
dc.identifier.issn2662-995X
dc.identifier.otherPURE: 78657697
dc.identifier.otherPURE UUID: 343bf17c-96d5-444e-8e27-71aa7c7b6501
dc.identifier.othercrossref: 10.1007/s42979-023-02106-3
dc.identifier.otherScopus: 85182451372
dc.identifier.otherORCID: /0000-0003-4732-3328/work/151426837
dc.identifier.urihttp://hdl.handle.net/10362/161429
dc.identifier.urlhttps://github.com/jespb/Python-StdGP
dc.identifier.urlhttps://github.com/jespb/Python-M3GP
dc.identifier.urlhttps://github.com/cavalab/m4gp-gametes
dc.identifier.urlhttps://github.com/NMVRodrigues/SLUG
dc.identifier.urlhttps://www.scopus.com/pages/publications/85182451372
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00408%2F2020/PT
dc.relationLASIGE - Extreme Computing
dc.relationLASIGE - Extreme Computing
dc.relationData Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal
dc.subjectFeature selection
dc.subjectEpistasis
dc.subjectGenetic Programming
dc.subjectGenetic algorithms
dc.subjectWrapper
dc.subjectLearner
dc.subjectMachine learning
dc.subjectGeneral Computer Science
dc.subjectComputer Science Applications
dc.subjectComputer Networks and Communications
dc.subjectComputer Graphics and Computer-Aided Design
dc.subjectComputational Theory and Mathematics
dc.subjectArtificial Intelligence
dc.titleExploring SLUGen
dc.title.subtitleFeature Selection Using Genetic Algorithms and Genetic Programmingen
dc.typejournal article
degois.publication.firstPage1
degois.publication.issue1
degois.publication.lastPage17
degois.publication.titleSN Computer Science
degois.publication.volume5
dspace.entity.typePublication
oaire.awardNumberUIDB/00408/2020
oaire.awardNumberUIDP/00408/2020
oaire.awardNumberDSAIPA/DS/0113/2019
oaire.awardTitleLASIGE - Extreme Computing
oaire.awardTitleLASIGE - Extreme Computing
oaire.awardTitleData Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00408%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00408%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0113%2F2019/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3eb06293-18be-490b-a68c-2da04a879a11
relation.isProjectOfPublicationd73f71a1-1b91-43fa-a3b8-df544c07753a
relation.isProjectOfPublicatione750f897-cfb5-46b7-84ff-3b768eeb44f6
relation.isProjectOfPublication.latestForDiscoveryd73f71a1-1b91-43fa-a3b8-df544c07753a

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