Towards the use of vector based GP to predict physiological time series

Detalhes bibliográficos
Autor(a) principal: Azzali, Irene
Data de Publicação: 2020
Outros Autores: Vanneschi, Leonardo, Bakurov, Illya, Silva, Sara, Ivaldi, Marco, Giacobini, Mario
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/92212
Resumo: Azzali, I., Vanneschi, L., Bakurov, I., Silva, S., Ivaldi, M., & Giacobini, M. (2020). Towards the use of vector based GP to predict physiological time series. Applied Soft Computing Journal, 89(April), [106097]. https://doi.org/10.1016/j.asoc.2020.106097-----------------------------------------------------------------This work was partially supported by FCT, Portugal, through funding of LASIGE Research Unit (UIDB/00408/2020) and projects INTERPHENO (PTDC/ASP-PLA/28726/2017), PERSEIDS (PTDC/EMS -SIS/0642/2014), OPTOX (PTDC/CTA-AMB/30056/2017), BINDER (PTDC/CCI-INF/29168/2017), AICE (DSAIPA/DS/0113/2019), GADgET (DSAIPA/DS/0022/2018), and PREDICT (PTDC/CCI-CIF/29877/2017). This study was also supported by Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR) under the programme "Dipartimenti di Eccellenza ex L.232/2016'' to the Department of Veterinary Science, University of Turin.
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spelling Towards the use of vector based GP to predict physiological time seriesGenetic programmingMachine learningPhysiological dataTime seriesVentilationSoftwareSDG 3 - Good Health and Well-beingAzzali, I., Vanneschi, L., Bakurov, I., Silva, S., Ivaldi, M., & Giacobini, M. (2020). Towards the use of vector based GP to predict physiological time series. Applied Soft Computing Journal, 89(April), [106097]. https://doi.org/10.1016/j.asoc.2020.106097-----------------------------------------------------------------This work was partially supported by FCT, Portugal, through funding of LASIGE Research Unit (UIDB/00408/2020) and projects INTERPHENO (PTDC/ASP-PLA/28726/2017), PERSEIDS (PTDC/EMS -SIS/0642/2014), OPTOX (PTDC/CTA-AMB/30056/2017), BINDER (PTDC/CCI-INF/29168/2017), AICE (DSAIPA/DS/0113/2019), GADgET (DSAIPA/DS/0022/2018), and PREDICT (PTDC/CCI-CIF/29877/2017). This study was also supported by Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR) under the programme "Dipartimenti di Eccellenza ex L.232/2016'' to the Department of Veterinary Science, University of Turin.Prediction of physiological time series is frequently approached by means of machine learning (ML) algorithms. However, most ML techniques are not able to directly manage time series, thus they do not exploit all the useful information such as patterns, peaks and regularities provided by the time dimension. Besides advanced ML methods such as recurrent neural network that preserve the ordered nature of time series, a recently developed approach of genetic programming, VE-GP, looks promising on the problem in analysis. VE-GP allows time series as terminals in the form of a vector, including new strategies to exploit this representation. In this paper we compare different ML techniques on the real problem of predicting ventilation flow from physiological variables with the aim of highlighting the potential of VE-GP. Experimental results show the advantage of applying this technique in the problem and we ascribe the good performances to the ability of properly catching meaningful information from time series.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAzzali, IreneVanneschi, LeonardoBakurov, IllyaSilva, SaraIvaldi, MarcoGiacobini, Mario2024-01-24T01:31:44Z2020-04-012020-04-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/92212eng1568-4946PURE: 16663109https://doi.org/10.1016/j.asoc.2020.106097info: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:RCAAP2024-03-11T04:41:03Zoai:run.unl.pt:10362/92212Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:37:28.630539Repositório 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 Towards the use of vector based GP to predict physiological time series
title Towards the use of vector based GP to predict physiological time series
spellingShingle Towards the use of vector based GP to predict physiological time series
Azzali, Irene
Genetic programming
Machine learning
Physiological data
Time series
Ventilation
Software
SDG 3 - Good Health and Well-being
title_short Towards the use of vector based GP to predict physiological time series
title_full Towards the use of vector based GP to predict physiological time series
title_fullStr Towards the use of vector based GP to predict physiological time series
title_full_unstemmed Towards the use of vector based GP to predict physiological time series
title_sort Towards the use of vector based GP to predict physiological time series
author Azzali, Irene
author_facet Azzali, Irene
Vanneschi, Leonardo
Bakurov, Illya
Silva, Sara
Ivaldi, Marco
Giacobini, Mario
author_role author
author2 Vanneschi, Leonardo
Bakurov, Illya
Silva, Sara
Ivaldi, Marco
Giacobini, Mario
author2_role author
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 Azzali, Irene
Vanneschi, Leonardo
Bakurov, Illya
Silva, Sara
Ivaldi, Marco
Giacobini, Mario
dc.subject.por.fl_str_mv Genetic programming
Machine learning
Physiological data
Time series
Ventilation
Software
SDG 3 - Good Health and Well-being
topic Genetic programming
Machine learning
Physiological data
Time series
Ventilation
Software
SDG 3 - Good Health and Well-being
description Azzali, I., Vanneschi, L., Bakurov, I., Silva, S., Ivaldi, M., & Giacobini, M. (2020). Towards the use of vector based GP to predict physiological time series. Applied Soft Computing Journal, 89(April), [106097]. https://doi.org/10.1016/j.asoc.2020.106097-----------------------------------------------------------------This work was partially supported by FCT, Portugal, through funding of LASIGE Research Unit (UIDB/00408/2020) and projects INTERPHENO (PTDC/ASP-PLA/28726/2017), PERSEIDS (PTDC/EMS -SIS/0642/2014), OPTOX (PTDC/CTA-AMB/30056/2017), BINDER (PTDC/CCI-INF/29168/2017), AICE (DSAIPA/DS/0113/2019), GADgET (DSAIPA/DS/0022/2018), and PREDICT (PTDC/CCI-CIF/29877/2017). This study was also supported by Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR) under the programme "Dipartimenti di Eccellenza ex L.232/2016'' to the Department of Veterinary Science, University of Turin.
publishDate 2020
dc.date.none.fl_str_mv 2020-04-01
2020-04-01T00:00:00Z
2024-01-24T01:31:44Z
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/10362/92212
url http://hdl.handle.net/10362/92212
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1568-4946
PURE: 16663109
https://doi.org/10.1016/j.asoc.2020.106097
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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