PL-k NN: A Parameterless Nearest Neighbors Classifier
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IWSSIP55020.2022.9854445 http://hdl.handle.net/11449/242235 |
Resumo: | Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The k-Nearest Neighbors is one of the most effective and straightforward models employed in numerous problems. Despite its well-known performance, it requires the value of k for specific data distribution, thus demanding expensive computational efforts. This paper proposes a k-Nearest Neighbors classifier that bypasses the need to define the value of k. The model computes the k value adaptively considering the data distribution of the training set. We compared the proposed model against the standard k-Nearest Neighbors classifier and two parameterless versions from the literature. Experiments over 11 public datasets confirm the robustness of the proposed approach, for the obtained results were similar or even better than its counterpart versions. |
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Repositório Institucional da UNESP |
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PL-k NN: A Parameterless Nearest Neighbors ClassifierClassificationClusteringk-Nearest NeighborsMachine LearningDemands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The k-Nearest Neighbors is one of the most effective and straightforward models employed in numerous problems. Despite its well-known performance, it requires the value of k for specific data distribution, thus demanding expensive computational efforts. This paper proposes a k-Nearest Neighbors classifier that bypasses the need to define the value of k. The model computes the k value adaptively considering the data distribution of the training set. We compared the proposed model against the standard k-Nearest Neighbors classifier and two parameterless versions from the literature. Experiments over 11 public datasets confirm the robustness of the proposed approach, for the obtained results were similar or even better than its counterpart versions.São Paulo State University Department of ComputingUniversity of Wolverhampton Cmi Lab School of Engineering and InformaticsSão Paulo State University Department of ComputingUniversidade Estadual Paulista (UNESP)Cmi Lab School of Engineering and InformaticsJodas, Danilo Samuel [UNESP]Passos, Leandro AparecidoAdeel, AhsanPapa, Joao Paulo [UNESP]2023-03-02T12:24:51Z2023-03-02T12:24:51Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/IWSSIP55020.2022.9854445International Conference on Systems, Signals, and Image Processing, v. 2022-June.2157-87022157-8672http://hdl.handle.net/11449/24223510.1109/IWSSIP55020.2022.98544452-s2.0-85137161569Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Conference on Systems, Signals, and Image Processinginfo:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/242235Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
PL-k NN: A Parameterless Nearest Neighbors Classifier |
title |
PL-k NN: A Parameterless Nearest Neighbors Classifier |
spellingShingle |
PL-k NN: A Parameterless Nearest Neighbors Classifier Jodas, Danilo Samuel [UNESP] Classification Clustering k-Nearest Neighbors Machine Learning |
title_short |
PL-k NN: A Parameterless Nearest Neighbors Classifier |
title_full |
PL-k NN: A Parameterless Nearest Neighbors Classifier |
title_fullStr |
PL-k NN: A Parameterless Nearest Neighbors Classifier |
title_full_unstemmed |
PL-k NN: A Parameterless Nearest Neighbors Classifier |
title_sort |
PL-k NN: A Parameterless Nearest Neighbors Classifier |
author |
Jodas, Danilo Samuel [UNESP] |
author_facet |
Jodas, Danilo Samuel [UNESP] Passos, Leandro Aparecido Adeel, Ahsan Papa, Joao Paulo [UNESP] |
author_role |
author |
author2 |
Passos, Leandro Aparecido Adeel, Ahsan Papa, Joao Paulo [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Cmi Lab School of Engineering and Informatics |
dc.contributor.author.fl_str_mv |
Jodas, Danilo Samuel [UNESP] Passos, Leandro Aparecido Adeel, Ahsan Papa, Joao Paulo [UNESP] |
dc.subject.por.fl_str_mv |
Classification Clustering k-Nearest Neighbors Machine Learning |
topic |
Classification Clustering k-Nearest Neighbors Machine Learning |
description |
Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The k-Nearest Neighbors is one of the most effective and straightforward models employed in numerous problems. Despite its well-known performance, it requires the value of k for specific data distribution, thus demanding expensive computational efforts. This paper proposes a k-Nearest Neighbors classifier that bypasses the need to define the value of k. The model computes the k value adaptively considering the data distribution of the training set. We compared the proposed model against the standard k-Nearest Neighbors classifier and two parameterless versions from the literature. Experiments over 11 public datasets confirm the robustness of the proposed approach, for the obtained results were similar or even better than its counterpart versions. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-03-02T12:24:51Z 2023-03-02T12:24:51Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/IWSSIP55020.2022.9854445 International Conference on Systems, Signals, and Image Processing, v. 2022-June. 2157-8702 2157-8672 http://hdl.handle.net/11449/242235 10.1109/IWSSIP55020.2022.9854445 2-s2.0-85137161569 |
url |
http://dx.doi.org/10.1109/IWSSIP55020.2022.9854445 http://hdl.handle.net/11449/242235 |
identifier_str_mv |
International Conference on Systems, Signals, and Image Processing, v. 2022-June. 2157-8702 2157-8672 10.1109/IWSSIP55020.2022.9854445 2-s2.0-85137161569 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Conference on Systems, Signals, and Image Processing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799965538176204800 |