GM4OS

Bibliographic Details
Main Author: Farinati, Davide
Publication Date: 2024
Other Authors: Vanneschi, Leonardo
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/165599
Summary: Farinati, D., & Vanneschi, L. (2024). GM4OS: An Evolutionary Oversampling Approach for Imbalanced Binary Classification Tasks. In S. Smith, J. Correia, & C. Cintrano (Eds.), Applications of Evolutionary Computation: 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings, Part I (Vol. 1, pp. 68-82). (Lecture Notes in Computer Science; Vol. 14634). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-56852-7_5 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
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spelling GM4OSAn Evolutionary Oversampling Approach for Imbalanced Binary Classification TasksOversamplingImbalanced DataBinary ClassificationGenetic ProgrammingGenetic AlgorithmsTheoretical Computer ScienceComputer Science(all)Farinati, D., & Vanneschi, L. (2024). GM4OS: An Evolutionary Oversampling Approach for Imbalanced Binary Classification Tasks. In S. Smith, J. Correia, & C. Cintrano (Eds.), Applications of Evolutionary Computation: 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings, Part I (Vol. 1, pp. 68-82). (Lecture Notes in Computer Science; Vol. 14634). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-56852-7_5 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.Imbalanced datasets pose a significant and longstanding challenge to machine learning algorithms, particularly in binary classification tasks. Over the past few years, various solutions have emerged, with a substantial focus on the automated generation of synthetic observations for the minority class, a technique known as oversampling. Among the various oversampling approaches, the Synthetic Minority Oversampling Technique (SMOTE) has recently garnered considerable attention as a highly promising method. SMOTE achieves this by generating new observations through the creation of points along the line segment connecting two existing minority class observations. Nevertheless, the performance of SMOTE frequently hinges upon the specific selection of these observation pairs for resampling. This research introduces the Genetic Methods for OverSampling (GM4OS), a novel oversampling technique that addresses this challenge. In GM4OS, individuals are represented as pairs of objects. The first object assumes the form of a GP-like function, operating on vectors, while the second object adopts a GA-like genome structure containing pairs of minority class observations. By co-evolving these two elements, GM4OS conducts a simultaneous search for the most suitable resampling pair and the most effective oversampling function. Experimental results, obtained on ten imbalanced binary classification problems, demonstrate that GM4OS consistently outperforms or yields results that are at least comparable to those achieved through linear regression and linear regression when combined with SMOTE.Springer Nature Switzerland AGInformation Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNFarinati, DavideVanneschi, Leonardo2025-03-22T01:33:33Z2024-04-212024-04-21T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion15application/pdfhttp://hdl.handle.net/10362/165599eng978-3-031-56851-00302-9743PURE: 86465388https://doi.org/10.1007/978-3-031-56852-7_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:RCAAP2025-03-31T01:52:11Zoai:run.unl.pt:10362/165599Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:50:49.099655Repositórios 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 GM4OS
An Evolutionary Oversampling Approach for Imbalanced Binary Classification Tasks
title GM4OS
spellingShingle GM4OS
Farinati, Davide
Oversampling
Imbalanced Data
Binary Classification
Genetic Programming
Genetic Algorithms
Theoretical Computer Science
Computer Science(all)
title_short GM4OS
title_full GM4OS
title_fullStr GM4OS
title_full_unstemmed GM4OS
title_sort GM4OS
author Farinati, Davide
author_facet Farinati, Davide
Vanneschi, Leonardo
author_role author
author2 Vanneschi, Leonardo
author2_role 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 Farinati, Davide
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Oversampling
Imbalanced Data
Binary Classification
Genetic Programming
Genetic Algorithms
Theoretical Computer Science
Computer Science(all)
topic Oversampling
Imbalanced Data
Binary Classification
Genetic Programming
Genetic Algorithms
Theoretical Computer Science
Computer Science(all)
description Farinati, D., & Vanneschi, L. (2024). GM4OS: An Evolutionary Oversampling Approach for Imbalanced Binary Classification Tasks. In S. Smith, J. Correia, & C. Cintrano (Eds.), Applications of Evolutionary Computation: 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings, Part I (Vol. 1, pp. 68-82). (Lecture Notes in Computer Science; Vol. 14634). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-56852-7_5 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
publishDate 2024
dc.date.none.fl_str_mv 2024-04-21
2024-04-21T00:00:00Z
2025-03-22T01:33:33Z
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/10362/165599
url http://hdl.handle.net/10362/165599
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-031-56851-0
0302-9743
PURE: 86465388
https://doi.org/10.1007/978-3-031-56852-7_5
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 15
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dc.publisher.none.fl_str_mv Springer Nature Switzerland AG
publisher.none.fl_str_mv Springer Nature Switzerland AG
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
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repository.mail.fl_str_mv info@rcaap.pt
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