A mutual information estimator for continuous and discrete variables applied to feature selection and classification problems

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dc.creatorFrederico Gualberto F. Coelho
dc.creatorAntonio P. Braga
dc.creatorMichel Verleysen
dc.date.accessioned2025-03-27T13:23:46Z
dc.date.accessioned2025-09-08T23:58:24Z
dc.date.available2025-03-27T13:23:46Z
dc.date.issued2016
dc.identifier.doi10.1080/18756891.2016.1204120
dc.identifier.issn1875-6883
dc.identifier.urihttps://hdl.handle.net/1843/80989
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInternational Journal of Computational Intelligence Systems
dc.rightsAcesso Aberto
dc.subjectInteligência artificial
dc.subjectAnálise numérica - Processamento de dados
dc.subject.otherinteligência computacional, Artificial Intelligence
dc.subject.otherSemissupervisionado
dc.subject.otherMutual Information
dc.subject.otherFeature Selection
dc.subject.otherinformation estimators that were specifically designed for continuous and for discrete variables
dc.titleA mutual information estimator for continuous and discrete variables applied to feature selection and classification problems
dc.typeArtigo de periódico
local.citation.epage733
local.citation.issue4
local.citation.spage726
local.citation.volume9
local.description.resumoCurrently Mutual Information has been widely used in pattern recognition and feature selection problems. It may be used as a measure of redundancy between features as well as a measure of dependency evaluating the relevance of each feature. Since marginal densities of real datasets are not usually known in advance, mutual information should be evaluated by estimation. There are mutual information estimators in the literature that were specifically designed for continuous or for discrete variables, however, most real problems are composed by a mixture of both. There is, of course, some implicit loss of information when using one of them to deal with mixed continuous and discrete variables. This paper presents a new estimator that is able to deal with mixed set of variables. It is shown in experiments with synthetic and real datasets that the method yields reliable results in such circumstance.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://link.springer.com/article/10.1080/18756891.2016.1204120

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