Machine Learning Methods with Noisy, Incomplete or Small Datasets

Autores
Caiafa, Cesar F.; Sun, Zhe; Tanaka, Toshihisa; Marti-Puig, Pere; Solé-Casals, Jordi
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.
Instituto Argentino de Radioastronomía
Materia
Ingeniería
Artificial intelligence
Imperfect dataset
Imperfect dataset
Machine learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/118855
Acceder
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network_name_str SEDICI (UNLP)
spelling Machine Learning Methods with Noisy, Incomplete or Small DatasetsCaiafa, Cesar F.Sun, ZheTanaka, ToshihisaMarti-Puig, PereSolé-Casals, JordiIngenieríaArtificial intelligenceImperfect datasetImperfect datasetMachine learningIn this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.Instituto Argentino de Radioastronomía2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/118855enginfo:eu-repo/semantics/altIdentifier/issn/2076-3417info:eu-repo/semantics/altIdentifier/doi/10.3390/app11094132info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:27:59Zoai:sedici.unlp.edu.ar:10915/118855Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:28:00.023SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Machine Learning Methods with Noisy, Incomplete or Small Datasets
title Machine Learning Methods with Noisy, Incomplete or Small Datasets
spellingShingle Machine Learning Methods with Noisy, Incomplete or Small Datasets
Caiafa, Cesar F.
Ingeniería
Artificial intelligence
Imperfect dataset
Imperfect dataset
Machine learning
title_short Machine Learning Methods with Noisy, Incomplete or Small Datasets
title_full Machine Learning Methods with Noisy, Incomplete or Small Datasets
title_fullStr Machine Learning Methods with Noisy, Incomplete or Small Datasets
title_full_unstemmed Machine Learning Methods with Noisy, Incomplete or Small Datasets
title_sort Machine Learning Methods with Noisy, Incomplete or Small Datasets
dc.creator.none.fl_str_mv Caiafa, Cesar F.
Sun, Zhe
Tanaka, Toshihisa
Marti-Puig, Pere
Solé-Casals, Jordi
author Caiafa, Cesar F.
author_facet Caiafa, Cesar F.
Sun, Zhe
Tanaka, Toshihisa
Marti-Puig, Pere
Solé-Casals, Jordi
author_role author
author2 Sun, Zhe
Tanaka, Toshihisa
Marti-Puig, Pere
Solé-Casals, Jordi
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ingeniería
Artificial intelligence
Imperfect dataset
Imperfect dataset
Machine learning
topic Ingeniería
Artificial intelligence
Imperfect dataset
Imperfect dataset
Machine learning
dc.description.none.fl_txt_mv In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.
Instituto Argentino de Radioastronomía
description In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/118855
url http://sedici.unlp.edu.ar/handle/10915/118855
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2076-3417
info:eu-repo/semantics/altIdentifier/doi/10.3390/app11094132
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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