SLUG

Bibliographic Details
Main Author: Rodrigues, Nuno M.
Publication Date: 2022
Other Authors: Batista, João E., La Cava, William, Vanneschi, Leonardo, Silva, Sara
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/142821
Summary: Rodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., & Silva, S. (2022). SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. In E. Medvet, G. Pappa, & B. Xue (Eds.), Genetic Programming: 25th European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (pp. 68-84). (Lecture Notes in Computer Science; Vol. 13223). Springer. https://doi.org/10.1007/978-3-031-02056-8_5 -------------------------------------------------------------------This work was supported by FCT, Portugal, through funding of LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020); MAR2020 program via project MarCODE (MAR-01.03.01-FEAMP-0047); projects BINDER (PTDC/CCI-INF/29168/2017), AICE (DSAIPA/DS/0113/2019), OPTOX (PTDC/CTA-AMB/30056/2017) and GADgET (DSAIPA/DS/0022/2018). 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 SLUGFeature Selection Using Genetic Algorithms and Genetic ProgrammingFeature SelectionEpistasisGenetic ProgrammingGenetic AlgorithmsMachine LearningTheoretical Computer ScienceComputer Science(all)Rodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., & Silva, S. (2022). SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. In E. Medvet, G. Pappa, & B. Xue (Eds.), Genetic Programming: 25th European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (pp. 68-84). (Lecture Notes in Computer Science; Vol. 13223). Springer. https://doi.org/10.1007/978-3-031-02056-8_5 -------------------------------------------------------------------This work was supported by FCT, Portugal, through funding of LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020); MAR2020 program via project MarCODE (MAR-01.03.01-FEAMP-0047); projects BINDER (PTDC/CCI-INF/29168/2017), AICE (DSAIPA/DS/0113/2019), OPTOX (PTDC/CTA-AMB/30056/2017) and GADgET (DSAIPA/DS/0022/2018). 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.We present SLUG, a method that uses genetic algorithms as a wrapper for genetic programming (GP), to perform feature selection while inducing models. This method is first tested on four regular binary classification datasets, and then on 10 synthetic datasets produced by GAMETES, a tool for embedding epistatic gene-gene interactions into noisy datasets. We compare the results of SLUG with the ones obtained by other GP-based methods that had already been used on the GAMETES problems, concluding that the proposed approach is very successful, particularly on the epistatic datasets. We discuss the merits and weaknesses of SLUG and its various parts, i.e. the wrapper and the learner, and we perform additional experiments, aimed at comparing SLUG with other state-of-the-art learners, like decision trees, random forests and extreme gradient boosting. Despite the fact that SLUG is not the most efficient method in terms of training time, it is confirmed as the most effective method in terms of accuracy.SpringerNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNRodrigues, Nuno M.Batista, João E.La Cava, WilliamVanneschi, LeonardoSilva, Sara2022-08-03T22:14:19Z2022-04-132022-04-13T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion17application/pdfhttp://hdl.handle.net/10362/142821eng978-3-031-02055-10302-9743PURE: 43491019https://doi.org/10.1007/978-3-031-02056-8_5info: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:RCAAP2024-05-22T18:04:14Zoai:run.unl.pt:10362/142821Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:34:46.675166Repositórios 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 SLUG
Feature Selection Using Genetic Algorithms and Genetic Programming
title SLUG
spellingShingle SLUG
Rodrigues, Nuno M.
Feature Selection
Epistasis
Genetic Programming
Genetic Algorithms
Machine Learning
Theoretical Computer Science
Computer Science(all)
title_short SLUG
title_full SLUG
title_fullStr SLUG
title_full_unstemmed SLUG
title_sort 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
Machine Learning
Theoretical Computer Science
Computer Science(all)
topic Feature Selection
Epistasis
Genetic Programming
Genetic Algorithms
Machine Learning
Theoretical Computer Science
Computer Science(all)
description Rodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., & Silva, S. (2022). SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. In E. Medvet, G. Pappa, & B. Xue (Eds.), Genetic Programming: 25th European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (pp. 68-84). (Lecture Notes in Computer Science; Vol. 13223). Springer. https://doi.org/10.1007/978-3-031-02056-8_5 -------------------------------------------------------------------This work was supported by FCT, Portugal, through funding of LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020); MAR2020 program via project MarCODE (MAR-01.03.01-FEAMP-0047); projects BINDER (PTDC/CCI-INF/29168/2017), AICE (DSAIPA/DS/0113/2019), OPTOX (PTDC/CTA-AMB/30056/2017) and GADgET (DSAIPA/DS/0022/2018). 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 2022
dc.date.none.fl_str_mv 2022-08-03T22:14:19Z
2022-04-13
2022-04-13T00:00:00Z
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url http://hdl.handle.net/10362/142821
dc.language.iso.fl_str_mv eng
language eng
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PURE: 43491019
https://doi.org/10.1007/978-3-031-02056-8_5
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 17
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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)
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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
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