A novel binary classification approach based on geometric semantic genetic programming

Detalhes bibliográficos
Autor(a) principal: Bakurov, Illya
Data de Publicação: 2022
Outros Autores: Castelli, Mauro, Fontanella, F., Scotto Di Freca, A., Vanneschi, Leonardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/129893
Resumo: Bakurov, I., Castelli, M., Fontanella, F., Scotto Di Freca, A., & Vanneschi, L. (2022). A novel binary classification approach based on geometric semantic genetic programming. Swarm and Evolutionary Computation, 69(March), 1-12. [101028]. https://doi.org/10.1016/j.swevo.2021.101028 ------Funding Information: This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCIINF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
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spelling A novel binary classification approach based on geometric semantic genetic programmingBinary classificationGeometric semantic genetic programmingComputer Science(all)Mathematics(all)Bakurov, I., Castelli, M., Fontanella, F., Scotto Di Freca, A., & Vanneschi, L. (2022). A novel binary classification approach based on geometric semantic genetic programming. Swarm and Evolutionary Computation, 69(March), 1-12. [101028]. https://doi.org/10.1016/j.swevo.2021.101028 ------Funding Information: This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCIINF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).Geometric semantic genetic programming (GSGP) is a recent variant of genetic programming. GSGP allows the landscape of any supervised regression problem to be transformed into a unimodal error surface, thus it has been applied only to this kind of problem. In a previous paper, we presented a novel variant of GSGP for binary classification problems that, taking inspiration from perceptron neural networks, uses a logistic-based activation function to constrain the output value of a GSGP tree in the interval [0,1]. This simple approach allowed us to use the standard RMSE function to evaluate the train classification error on binary classification problems and, consequently, to preserve the intrinsic properties of the geometric semantic operators. The results encouraged us to investigate this approach further. To this aim, in this paper, we present the results from 18 test problems, which we compared with those achieved by eleven well-known and widely classification schemes. We also studied how the parameter settings affect the classification performance and the use of the -score function to deal with imbalanced data. The results confirmed the effectiveness of the proposed approach.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNBakurov, IllyaCastelli, MauroFontanella, F.Scotto Di Freca, A.Vanneschi, Leonardo2024-01-24T01:31:43Z2022-03-012022-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttp://hdl.handle.net/10362/129893eng2210-6502PURE: 35605518https://doi.org/10.1016/j.swevo.2021.101028info: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:RCAAP2024-03-11T05:08:51Zoai:run.unl.pt:10362/129893Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:40.501069Repositório 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 A novel binary classification approach based on geometric semantic genetic programming
title A novel binary classification approach based on geometric semantic genetic programming
spellingShingle A novel binary classification approach based on geometric semantic genetic programming
Bakurov, Illya
Binary classification
Geometric semantic genetic programming
Computer Science(all)
Mathematics(all)
title_short A novel binary classification approach based on geometric semantic genetic programming
title_full A novel binary classification approach based on geometric semantic genetic programming
title_fullStr A novel binary classification approach based on geometric semantic genetic programming
title_full_unstemmed A novel binary classification approach based on geometric semantic genetic programming
title_sort A novel binary classification approach based on geometric semantic genetic programming
author Bakurov, Illya
author_facet Bakurov, Illya
Castelli, Mauro
Fontanella, F.
Scotto Di Freca, A.
Vanneschi, Leonardo
author_role author
author2 Castelli, Mauro
Fontanella, F.
Scotto Di Freca, A.
Vanneschi, Leonardo
author2_role author
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 Bakurov, Illya
Castelli, Mauro
Fontanella, F.
Scotto Di Freca, A.
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Binary classification
Geometric semantic genetic programming
Computer Science(all)
Mathematics(all)
topic Binary classification
Geometric semantic genetic programming
Computer Science(all)
Mathematics(all)
description Bakurov, I., Castelli, M., Fontanella, F., Scotto Di Freca, A., & Vanneschi, L. (2022). A novel binary classification approach based on geometric semantic genetic programming. Swarm and Evolutionary Computation, 69(March), 1-12. [101028]. https://doi.org/10.1016/j.swevo.2021.101028 ------Funding Information: This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCIINF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
publishDate 2022
dc.date.none.fl_str_mv 2022-03-01
2022-03-01T00:00:00Z
2024-01-24T01:31:43Z
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/10362/129893
url http://hdl.handle.net/10362/129893
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2210-6502
PURE: 35605518
https://doi.org/10.1016/j.swevo.2021.101028
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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