Machine Learning Methods with Noisy, Incomplete or Small Datasets

Autores
Caiafa, César Federico; Zhe, Sun; 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.
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Zhe, Sun. Lab. Adaptive Intelligence - Riken; Japón
Fil: Tanaka, Toshihisa. Tokyo University of Agriculture and Technology; Japón
Fil: Marti Puig, Pere. University of Vic; España
Fil: Solé Casals, Jordi. University of Vic; España
Materia
Machine learning
artificial intelligence
neural networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/135279
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Machine Learning Methods with Noisy, Incomplete or Small DatasetsCaiafa, César FedericoZhe, SunTanaka, ToshihisaMarti Puig, PereSolé Casals, JordiMachine learningartificial intelligenceneural networkshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In 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.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Zhe, Sun. Lab. Adaptive Intelligence - Riken; JapónFil: Tanaka, Toshihisa. Tokyo University of Agriculture and Technology; JapónFil: Marti Puig, Pere. University of Vic; EspañaFil: Solé Casals, Jordi. University of Vic; EspañaMDPI2021-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/135279Caiafa, César Federico; Zhe, Sun ; Tanaka, Toshihisa ; Marti Puig, Pere; Solé Casals, Jordi; Machine Learning Methods with Noisy, Incomplete or Small Datasets; MDPI; Applied Sciences; 11; 9; 4-2021; 1-42076-3417CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2076-3417/11/9/4132info:eu-repo/semantics/altIdentifier/doi/10.3390/app11094132info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:00:31Zoai:ri.conicet.gov.ar:11336/135279instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:00:31.632CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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, César Federico
Machine learning
artificial intelligence
neural networks
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, César Federico
Zhe, Sun
Tanaka, Toshihisa
Marti Puig, Pere
Solé Casals, Jordi
author Caiafa, César Federico
author_facet Caiafa, César Federico
Zhe, Sun
Tanaka, Toshihisa
Marti Puig, Pere
Solé Casals, Jordi
author_role author
author2 Zhe, Sun
Tanaka, Toshihisa
Marti Puig, Pere
Solé Casals, Jordi
author2_role author
author
author
author
dc.subject.none.fl_str_mv Machine learning
artificial intelligence
neural networks
topic Machine learning
artificial intelligence
neural networks
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
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.
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Zhe, Sun. Lab. Adaptive Intelligence - Riken; Japón
Fil: Tanaka, Toshihisa. Tokyo University of Agriculture and Technology; Japón
Fil: Marti Puig, Pere. University of Vic; España
Fil: Solé Casals, Jordi. University of Vic; España
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 2021-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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://hdl.handle.net/11336/135279
Caiafa, César Federico; Zhe, Sun ; Tanaka, Toshihisa ; Marti Puig, Pere; Solé Casals, Jordi; Machine Learning Methods with Noisy, Incomplete or Small Datasets; MDPI; Applied Sciences; 11; 9; 4-2021; 1-4
2076-3417
CONICET Digital
CONICET
url http://hdl.handle.net/11336/135279
identifier_str_mv Caiafa, César Federico; Zhe, Sun ; Tanaka, Toshihisa ; Marti Puig, Pere; Solé Casals, Jordi; Machine Learning Methods with Noisy, Incomplete or Small Datasets; MDPI; Applied Sciences; 11; 9; 4-2021; 1-4
2076-3417
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2076-3417/11/9/4132
info:eu-repo/semantics/altIdentifier/doi/10.3390/app11094132
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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