Machine Learning Methods with Noisy, Incomplete or Small Datasets

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)....

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Detalles Bibliográficos
Autores principales: Caiafa, Cesar F., Sun, Zhe, Tanaka, Toshihisa, Marti-Puig, Pere, Solé-Casals, Jordi
Formato: Articulo
Lenguaje:Inglés
Publicado: 2021
Materias:
Ingeniería
Artificial intelligence
Imperfect dataset
Machine learning
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/118855
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-118855
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ingeniería
Artificial intelligence
Imperfect dataset
Imperfect dataset
Machine learning
spellingShingle Ingeniería
Artificial intelligence
Imperfect dataset
Imperfect dataset
Machine learning
Caiafa, Cesar F.
Sun, Zhe
Tanaka, Toshihisa
Marti-Puig, Pere
Solé-Casals, Jordi
Machine Learning Methods with Noisy, Incomplete or Small Datasets
topic_facet Ingeniería
Artificial intelligence
Imperfect dataset
Imperfect dataset
Machine learning
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.
format Articulo
Articulo
author Caiafa, Cesar F.
Sun, Zhe
Tanaka, Toshihisa
Marti-Puig, Pere
Solé-Casals, Jordi
author_facet Caiafa, Cesar F.
Sun, Zhe
Tanaka, Toshihisa
Marti-Puig, Pere
Solé-Casals, Jordi
author_sort Caiafa, Cesar F.
title Machine Learning Methods with Noisy, Incomplete or Small Datasets
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
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/118855
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AT sunzhe machinelearningmethodswithnoisyincompleteorsmalldatasets
AT tanakatoshihisa machinelearningmethodswithnoisyincompleteorsmalldatasets
AT martipuigpere machinelearningmethodswithnoisyincompleteorsmalldatasets
AT solecasalsjordi machinelearningmethodswithnoisyincompleteorsmalldatasets
bdutipo_str Repositorios
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