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An unsupervised approach to feature discretization and selection

2012Journal article Restricted access
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dc.contributor.authorJ. Ferreira, Artur
dc.contributor.authorFigueiredo, Mário A. T.
dc.date.accessioned2018-06-06T08:56:51Z
dc.date.available2018-06-06T08:56:51Z
dc.date.issued2012
dc.description.abstractMany learning problems require handling high dimensional data sets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevante (oreven detrimental) for the learning tasks. It ist hus clear that the reisaneed for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for médium and high-dimensional datas ets. The experimental results on several standard data sets, with both sparse and dense features, showthe efficiency of the proposed techniques as well as improvements over previous related techniques.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFERREIRA, Artur Jorge; FIGUEIREDO, Mário A. T. – An unsupervised approach to feature discretization and selection. Pattern Recognition. ISSN 0031-3203. Vol. 45, (2012), pp. 3048-3060.pt_PT
dc.identifier.issn0031-3203
dc.identifier.urihttp://hdl.handle.net/10400.21/8569
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationSFRH/PROTEC/67605/2010pt_PT
dc.relationStrategic Project - LA 8 - 2011-2012
dc.subjectFeature discretizationpt_PT
dc.subjectFeature quantizationpt_PT
dc.subjectFeature selectionpt_PT
dc.subjectLinde–Buzo–Gray algorithmpt_PT
dc.subjectSparse datapt_PT
dc.subjectSupport vectormachinespt_PT
dc.subjectNaïve Bayespt_PT
dc.subjectK-nearest neighborpt_PT
dc.titleAn unsupervised approach to feature discretization and selectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleStrategic Project - LA 8 - 2011-2012
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/PEst-OE%2FEEI%2FLA0008%2F2011/PT
oaire.citation.endPage3060pt_PT
oaire.citation.startPage3048pt_PT
oaire.citation.titlePattern Recognitionpt_PT
oaire.citation.volume45pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFerreira
person.givenNameArtur
person.identifier1049438
person.identifier.ciencia-id091A-96FB-A88C
person.identifier.orcid0000-0002-6508-0932
person.identifier.ridAAL-4377-2020
person.identifier.scopus-author-id35315359300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication734bfe75-0c68-4cdf-8a87-2aef3564f5bd
relation.isAuthorOfPublication.latestForDiscovery734bfe75-0c68-4cdf-8a87-2aef3564f5bd
relation.isProjectOfPublication9fabd534-dda1-43e9-928f-4f085227453a
relation.isProjectOfPublication.latestForDiscovery9fabd534-dda1-43e9-928f-4f085227453a

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