M6GP

Detalhes bibliográficos
Autor(a) principal: Batista, João Eduardo
Data de Publicação: 2024
Outros Autores: Rodrigues, Nuno Miguel, Vanneschi, Leonardo, Silva, Sara
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/172920
Resumo: Batista, J. E., Rodrigues, N. M., Vanneschi, L., & Silva, S. (2024). M6GP: Multiobjective Feature Engineering. In 2024 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC60901.2024.10612107 --- This work was supported by FCT through the LASIGE (UIDB/00408/20203 and UIDP/00408/20204) and MagIC/NOVA IMS (UIDB/04l52/2020) research units, and PhD Grant 202l/05322/BD
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spelling M6GPMultiobjective Feature EngineeringGenetic ProgrammingMultiobjective OptimizationFeature EngineeringExplainable AIInterpretabilityArtificial IntelligenceComputer Science ApplicationsComputer Vision and Pattern RecognitionComputational MathematicsControl and OptimizationBatista, J. E., Rodrigues, N. M., Vanneschi, L., & Silva, S. (2024). M6GP: Multiobjective Feature Engineering. In 2024 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC60901.2024.10612107 --- This work was supported by FCT through the LASIGE (UIDB/00408/20203 and UIDP/00408/20204) and MagIC/NOVA IMS (UIDB/04l52/2020) research units, and PhD Grant 202l/05322/BDThe current trend in machine learning is to use powerful algorithms to induce complex predictive models that often fall under the category of “black-box models”. Thanks to this, there is also a growing interest in studying model explainabil-ity and interpretability so that human experts can understand, validate, and correct those models. With the objective of promoting the creation of inherently interpretable models, we present M6GP. This wrapper-based multi-objective automatic feature engineering algorithm combines key components of the M3GP and NSGA-II algorithms. Wrapping M6GP around another machine learning algorithm evolves a set of features optimized for this algorithm while potentially increasing its robustness. We compare our results with M3GP and M4GP, two ancestors from the same algorithm family, and verify that, by using a multi-objective approach, M6GP obtains equal or better results. In addition, by using complexity metrics on the list of objectives, the M6GP models come down to one-fifth of the size of the M3GP models, making them easier to read by comparison.Institute of Electrical and Electronics Engineers (IEEE)Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNBatista, João EduardoRodrigues, Nuno MiguelVanneschi, LeonardoSilva, Sara20242026-08-08T00:00:00Z2024-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion8application/pdfhttp://hdl.handle.net/10362/172920eng979-8-3503-0837-2PURE: 98688434https://doi.org/10.1109/CEC60901.2024.10612107info:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-10-07T01:41:14Zoai:run.unl.pt:10362/172920Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:55:28.001261Repositórios 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 M6GP
Multiobjective Feature Engineering
title M6GP
spellingShingle M6GP
Batista, João Eduardo
Genetic Programming
Multiobjective Optimization
Feature Engineering
Explainable AI
Interpretability
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Computational Mathematics
Control and Optimization
title_short M6GP
title_full M6GP
title_fullStr M6GP
title_full_unstemmed M6GP
title_sort M6GP
author Batista, João Eduardo
author_facet Batista, João Eduardo
Rodrigues, Nuno Miguel
Vanneschi, Leonardo
Silva, Sara
author_role author
author2 Rodrigues, Nuno Miguel
Vanneschi, Leonardo
Silva, Sara
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 Batista, João Eduardo
Rodrigues, Nuno Miguel
Vanneschi, Leonardo
Silva, Sara
dc.subject.por.fl_str_mv Genetic Programming
Multiobjective Optimization
Feature Engineering
Explainable AI
Interpretability
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Computational Mathematics
Control and Optimization
topic Genetic Programming
Multiobjective Optimization
Feature Engineering
Explainable AI
Interpretability
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Computational Mathematics
Control and Optimization
description Batista, J. E., Rodrigues, N. M., Vanneschi, L., & Silva, S. (2024). M6GP: Multiobjective Feature Engineering. In 2024 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC60901.2024.10612107 --- This work was supported by FCT through the LASIGE (UIDB/00408/20203 and UIDP/00408/20204) and MagIC/NOVA IMS (UIDB/04l52/2020) research units, and PhD Grant 202l/05322/BD
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01T00:00:00Z
2026-08-08T00:00:00Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/172920
url http://hdl.handle.net/10362/172920
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 979-8-3503-0837-2
PURE: 98688434
https://doi.org/10.1109/CEC60901.2024.10612107
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv 8
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
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
repository.mail.fl_str_mv info@rcaap.pt
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