Improving the quality of predictive models in small data GSDOT

Detalhes bibliográficos
Autor(a) principal: Douzas, Georgios
Data de Publicação: 2022
Outros Autores: Lechleitner, Maria, Bacao, Fernando
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/136541
Resumo: Douzas, G., Lechleitner, M., & Bacao, F. (2022). Improving the quality of predictive models in small data GSDOT: A new algorithm for generating synthetic data. PLoS ONE, 17(4), 1-15. [e0265626]. https://doi.org/10.1371/journal.pone.0265626
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spelling Improving the quality of predictive models in small data GSDOTA new algorithm for generating synthetic dataGeneralDouzas, G., Lechleitner, M., & Bacao, F. (2022). Improving the quality of predictive models in small data GSDOT: A new algorithm for generating synthetic data. PLoS ONE, 17(4), 1-15. [e0265626]. https://doi.org/10.1371/journal.pone.0265626In the age of the data deluge there are still many domains and applications restricted to the use of small datasets. The ability to harness these small datasets to solve problems through the use of supervised learning methods can have a significant impact in many important areas. The insufficient size of training data usually results in unsatisfactory performance of machine learning algorithms. The current research work aims to contribute to mitigate the small data problem through the creation of artificial instances, which are added to the training process. The proposed algorithm, Geometric Small Data Oversampling Technique, uses geometric regions around existing samples to generate new high quality instances. Experimental results show a significant improvement in accuracy when compared with the use of the initial small dataset as well as other popular artificial data generation techniques.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNDouzas, GeorgiosLechleitner, MariaBacao, Fernando2022-04-16T22:38:24Z2022-04-072022-04-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15application/pdfhttp://hdl.handle.net/10362/136541eng1932-6203PURE: 43275911https://doi.org/10.1371/journal.pone.0265626info: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:RCAAP2024-03-11T05:14:36Zoai:run.unl.pt:10362/136541Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:43.875261Repositório 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 Improving the quality of predictive models in small data GSDOT
A new algorithm for generating synthetic data
title Improving the quality of predictive models in small data GSDOT
spellingShingle Improving the quality of predictive models in small data GSDOT
Douzas, Georgios
General
title_short Improving the quality of predictive models in small data GSDOT
title_full Improving the quality of predictive models in small data GSDOT
title_fullStr Improving the quality of predictive models in small data GSDOT
title_full_unstemmed Improving the quality of predictive models in small data GSDOT
title_sort Improving the quality of predictive models in small data GSDOT
author Douzas, Georgios
author_facet Douzas, Georgios
Lechleitner, Maria
Bacao, Fernando
author_role author
author2 Lechleitner, Maria
Bacao, Fernando
author2_role 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 Douzas, Georgios
Lechleitner, Maria
Bacao, Fernando
dc.subject.por.fl_str_mv General
topic General
description Douzas, G., Lechleitner, M., & Bacao, F. (2022). Improving the quality of predictive models in small data GSDOT: A new algorithm for generating synthetic data. PLoS ONE, 17(4), 1-15. [e0265626]. https://doi.org/10.1371/journal.pone.0265626
publishDate 2022
dc.date.none.fl_str_mv 2022-04-16T22:38:24Z
2022-04-07
2022-04-07T00:00:00Z
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dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/136541
url http://hdl.handle.net/10362/136541
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1932-6203
PURE: 43275911
https://doi.org/10.1371/journal.pone.0265626
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eu_rights_str_mv openAccess
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