Supervised non-parametric discretization based on Kernel density estimation

Nowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main goal being to retain as much discriminative information as possible. In this...

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Detalles Bibliográficos
Autores: Flores, J.L., Calvo, B., Pérez, A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/1091
Acceso en línea:http://hdl.handle.net/20.500.11824/1091
Access Level:acceso abierto
Palabra clave:Discretization
Supervised classification
Non-parametric
Kernel density estimation
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spelling Supervised non-parametric discretization based on Kernel density estimationFlores, J.L.Calvo, B.Pérez, A.DiscretizationSupervised classificationNon-parametricKernel density estimationNowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main goal being to retain as much discriminative information as possible. In this paper, we propose a supervised univariate non-parametric discretization algorithm which allows the use of a given supervised score criterion for selecting the best cut points. The candidate cut points are evaluated by computing the selected score value using kernel density estimation. The computational complexity of the proposed procedure is O(N log N), where N is the length of the data. Our proposed algorithm generates a low complexity in discretization policies while retaining the discriminative information of the original continuous variables. In order to assess the validity of the proposed method, a set of real and artificial datasets has been used and the results show that the algorithm provides competitive results in terms of performance, a low complexity in the discretization policies and a high performance.202020202019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/1091reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Ingléshttps://www.sciencedirect.com/science/article/abs/pii/S0167865519302958info:eu-repo/grantAgreement/MINECO//SEV-2017-0718info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/TIN2017-82626-Rinfo:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2018-2021info:eu-repo/grantAgreement/Gobierno Vasco/ELKARTEK/Reconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/10912026-06-19T12:47:47Z
dc.title.none.fl_str_mv Supervised non-parametric discretization based on Kernel density estimation
title Supervised non-parametric discretization based on Kernel density estimation
spellingShingle Supervised non-parametric discretization based on Kernel density estimation
Flores, J.L.
Discretization
Supervised classification
Non-parametric
Kernel density estimation
title_short Supervised non-parametric discretization based on Kernel density estimation
title_full Supervised non-parametric discretization based on Kernel density estimation
title_fullStr Supervised non-parametric discretization based on Kernel density estimation
title_full_unstemmed Supervised non-parametric discretization based on Kernel density estimation
title_sort Supervised non-parametric discretization based on Kernel density estimation
dc.creator.none.fl_str_mv Flores, J.L.
Calvo, B.
Pérez, A.
author Flores, J.L.
author_facet Flores, J.L.
Calvo, B.
Pérez, A.
author_role author
author2 Calvo, B.
Pérez, A.
author2_role author
author
dc.subject.none.fl_str_mv Discretization
Supervised classification
Non-parametric
Kernel density estimation
topic Discretization
Supervised classification
Non-parametric
Kernel density estimation
description Nowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main goal being to retain as much discriminative information as possible. In this paper, we propose a supervised univariate non-parametric discretization algorithm which allows the use of a given supervised score criterion for selecting the best cut points. The candidate cut points are evaluated by computing the selected score value using kernel density estimation. The computational complexity of the proposed procedure is O(N log N), where N is the length of the data. Our proposed algorithm generates a low complexity in discretization policies while retaining the discriminative information of the original continuous variables. In order to assess the validity of the proposed method, a set of real and artificial datasets has been used and the results show that the algorithm provides competitive results in terms of performance, a low complexity in the discretization policies and a high performance.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/1091
url http://hdl.handle.net/20.500.11824/1091
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/abs/pii/S0167865519302958
info:eu-repo/grantAgreement/MINECO//SEV-2017-0718
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/TIN2017-82626-R
info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2018-2021
info:eu-repo/grantAgreement/Gobierno Vasco/ELKARTEK/
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
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