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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/118855
Ver los metadatos del registro completo
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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:UNLP2025-09-03T11:00:06Zoai: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:13292025-09-03 11:00:07.044SEDICI (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 |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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