Variable selection for Naïve Bayes classification

The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naïve Bayes’ assumption of conditional independence, and may deteriorate the method’s performance. Moreover, datasets are...

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Detalles Bibliográficos
Autores: Blanquero Bravo, Rafael, Carrizosa Priego, Emilio José, Ramírez Cobo, Josefa, Sillero Denamiel, María Remedios
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/134720
Acceso en línea:https://hdl.handle.net/11441/134720
https://doi.org/10.1016/j.cor.2021.105456
Access Level:acceso abierto
Palabra clave:Clustering
Conditional independence
Dependence measures
Heuristics
Probabilistic classification
Cost-sensitive classification
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spelling Variable selection for Naïve Bayes classificationBlanquero Bravo, RafaelCarrizosa Priego, Emilio JoséRamírez Cobo, JosefaSillero Denamiel, María RemediosClusteringConditional independenceDependence measuresHeuristicsProbabilistic classificationCost-sensitive classificationThe Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naïve Bayes’ assumption of conditional independence, and may deteriorate the method’s performance. Moreover, datasets are often characterized by a large number of features, which may complicate the interpretation of the results as well as slow down the method’s execution. In this paper we propose a sparse version of the Naïve Bayes classifier that is characterized by three properties. First, the sparsity is achieved taking into account the correlation structure of the covariates. Second, different performance measures can be used to guide the selection of features. Third, performance constraints on groups of higher interest can be included. Our proposal leads to a smart search, which yields competitive running times, whereas the flexibility in terms of performance measure for classification is integrated. Our findings show that, when compared against well-referenced feature selection approaches, the proposed sparse Naïve Bayes obtains competitive results regarding accuracy, sparsity and running times for balanced datasets. In the case of datasets with unbalanced (or with different importance) classes, a better compromise between classification rates for the different classes is achieved.ElsevierEstadística e Investigación Operativa2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/134720https://doi.org/10.1016/j.cor.2021.105456reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésComputers and Operations Research, 135, 2-11.https://doi.org/10.1016/j.cor.2021.105456info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1347202026-06-17T12:51:07Z
dc.title.none.fl_str_mv Variable selection for Naïve Bayes classification
title Variable selection for Naïve Bayes classification
spellingShingle Variable selection for Naïve Bayes classification
Blanquero Bravo, Rafael
Clustering
Conditional independence
Dependence measures
Heuristics
Probabilistic classification
Cost-sensitive classification
title_short Variable selection for Naïve Bayes classification
title_full Variable selection for Naïve Bayes classification
title_fullStr Variable selection for Naïve Bayes classification
title_full_unstemmed Variable selection for Naïve Bayes classification
title_sort Variable selection for Naïve Bayes classification
dc.creator.none.fl_str_mv Blanquero Bravo, Rafael
Carrizosa Priego, Emilio José
Ramírez Cobo, Josefa
Sillero Denamiel, María Remedios
author Blanquero Bravo, Rafael
author_facet Blanquero Bravo, Rafael
Carrizosa Priego, Emilio José
Ramírez Cobo, Josefa
Sillero Denamiel, María Remedios
author_role author
author2 Carrizosa Priego, Emilio José
Ramírez Cobo, Josefa
Sillero Denamiel, María Remedios
author2_role author
author
author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
dc.subject.none.fl_str_mv Clustering
Conditional independence
Dependence measures
Heuristics
Probabilistic classification
Cost-sensitive classification
topic Clustering
Conditional independence
Dependence measures
Heuristics
Probabilistic classification
Cost-sensitive classification
description The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naïve Bayes’ assumption of conditional independence, and may deteriorate the method’s performance. Moreover, datasets are often characterized by a large number of features, which may complicate the interpretation of the results as well as slow down the method’s execution. In this paper we propose a sparse version of the Naïve Bayes classifier that is characterized by three properties. First, the sparsity is achieved taking into account the correlation structure of the covariates. Second, different performance measures can be used to guide the selection of features. Third, performance constraints on groups of higher interest can be included. Our proposal leads to a smart search, which yields competitive running times, whereas the flexibility in terms of performance measure for classification is integrated. Our findings show that, when compared against well-referenced feature selection approaches, the proposed sparse Naïve Bayes obtains competitive results regarding accuracy, sparsity and running times for balanced datasets. In the case of datasets with unbalanced (or with different importance) classes, a better compromise between classification rates for the different classes is achieved.
publishDate 2021
dc.date.none.fl_str_mv 2021
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 https://hdl.handle.net/11441/134720
https://doi.org/10.1016/j.cor.2021.105456
url https://hdl.handle.net/11441/134720
https://doi.org/10.1016/j.cor.2021.105456
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Computers and Operations Research, 135, 2-11.
https://doi.org/10.1016/j.cor.2021.105456
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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