Single and Multi-objective Genetic Programming Methods for Prediction Intervals

Detalhes bibliográficos
Autor(a) principal: Brotto Rebuli, Karina
Data de Publicação: 2023
Outros Autores: Giacobini, Mario, Tallone, Niccolò, Vanneschi, Leonardo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/162074
Resumo: Brotto Rebuli, K., Giacobini, M., Tallone, N., & Vanneschi, L. (2023). Single and Multi-objective Genetic Programming Methods for Prediction Intervals. In C. de Stefano, F. Fontanella, & L. Vanneschi (Eds.), Artificial Life and Evolutionary Computation: 16th Italian Workshop, WIVACE 2022, Gaeta, Italy, September 14–16, 2022, Revised Selected Papers (pp. 205-218). (Communications in Computer and Information Science; Vol. 1780). Springer, Cham. https://doi.org/10.1007/978-3-031-31183-3_17
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spelling Single and Multi-objective Genetic Programming Methods for Prediction IntervalsPrediction IntervalCrisp predictionModelling uncertaintyMachine LearningComputer Science(all)Mathematics(all)Brotto Rebuli, K., Giacobini, M., Tallone, N., & Vanneschi, L. (2023). Single and Multi-objective Genetic Programming Methods for Prediction Intervals. In C. de Stefano, F. Fontanella, & L. Vanneschi (Eds.), Artificial Life and Evolutionary Computation: 16th Italian Workshop, WIVACE 2022, Gaeta, Italy, September 14–16, 2022, Revised Selected Papers (pp. 205-218). (Communications in Computer and Information Science; Vol. 1780). Springer, Cham. https://doi.org/10.1007/978-3-031-31183-3_17A PI is the range of values in which the real target value of a supervised learning task is expected to fall into, and it should combine two contrasting properties: to be as narrow as possible, and to include as many data observations as possible. This article presents an study on modelling Prediction Intervals (PI) with two Genetic Programming (GP) methods. The first proposed GP method is called CWC-GP, and it evolves simultaneously the lower and upper boundaries of the PI using a single fitness measure. This measure is the Coverage Width-based Criterion (CWC), which combines the width and the probability coverage of the PI. The second proposed GP method is called LUBE-GP, and it evolves independently the lower and upper boundaries of the PI. This method applies a multi-objective approach, in which one fitness aims to minimise the width and the other aims to maximise the probability coverage of the PI. Both methods were applied with the Direct and the Sequential approaches. In the former, the PI is assessed without the crisp prediction of the model. In the latter, the method makes use of the crisp prediction to find the PI boundaries. The proposed methods showed to have good potential on assessing PIs and the results pave the way to further investigations.Springer, ChamNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBrotto Rebuli, KarinaGiacobini, MarioTallone, NiccolòVanneschi, Leonardo2024-05-01T00:32:42Z2023-04-302023-04-30T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion14application/pdfhttp://hdl.handle.net/10362/162074eng978-3-031-31182-61865-0929PURE: 59818198https://doi.org/10.1007/978-3-031-31183-3_17info: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:RCAAP2024-05-22T18:17:13Zoai:run.unl.pt:10362/162074Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:47:51.278845Repositórios 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 Single and Multi-objective Genetic Programming Methods for Prediction Intervals
title Single and Multi-objective Genetic Programming Methods for Prediction Intervals
spellingShingle Single and Multi-objective Genetic Programming Methods for Prediction Intervals
Brotto Rebuli, Karina
Prediction Interval
Crisp prediction
Modelling uncertainty
Machine Learning
Computer Science(all)
Mathematics(all)
title_short Single and Multi-objective Genetic Programming Methods for Prediction Intervals
title_full Single and Multi-objective Genetic Programming Methods for Prediction Intervals
title_fullStr Single and Multi-objective Genetic Programming Methods for Prediction Intervals
title_full_unstemmed Single and Multi-objective Genetic Programming Methods for Prediction Intervals
title_sort Single and Multi-objective Genetic Programming Methods for Prediction Intervals
author Brotto Rebuli, Karina
author_facet Brotto Rebuli, Karina
Giacobini, Mario
Tallone, Niccolò
Vanneschi, Leonardo
author_role author
author2 Giacobini, Mario
Tallone, Niccolò
Vanneschi, Leonardo
author2_role 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 Brotto Rebuli, Karina
Giacobini, Mario
Tallone, Niccolò
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Prediction Interval
Crisp prediction
Modelling uncertainty
Machine Learning
Computer Science(all)
Mathematics(all)
topic Prediction Interval
Crisp prediction
Modelling uncertainty
Machine Learning
Computer Science(all)
Mathematics(all)
description Brotto Rebuli, K., Giacobini, M., Tallone, N., & Vanneschi, L. (2023). Single and Multi-objective Genetic Programming Methods for Prediction Intervals. In C. de Stefano, F. Fontanella, & L. Vanneschi (Eds.), Artificial Life and Evolutionary Computation: 16th Italian Workshop, WIVACE 2022, Gaeta, Italy, September 14–16, 2022, Revised Selected Papers (pp. 205-218). (Communications in Computer and Information Science; Vol. 1780). Springer, Cham. https://doi.org/10.1007/978-3-031-31183-3_17
publishDate 2023
dc.date.none.fl_str_mv 2023-04-30
2023-04-30T00:00:00Z
2024-05-01T00:32:42Z
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/10362/162074
url http://hdl.handle.net/10362/162074
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-031-31182-6
1865-0929
PURE: 59818198
https://doi.org/10.1007/978-3-031-31183-3_17
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
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dc.publisher.none.fl_str_mv Springer, Cham
publisher.none.fl_str_mv Springer, Cham
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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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
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