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Efficient feature selection filters for high-dimensional data

dc.contributor.authorJ. Ferreira, Artur
dc.contributor.authorFigueiredo, Mário A. T.
dc.date.accessioned2015-09-07T13:27:31Z
dc.date.available2015-09-07T13:27:31Z
dc.date.issued2012-10-01
dc.description.abstractFeature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.por
dc.identifier.citationFERREIRA, Artur J.; FIGUEIREDO, Mário A. T. – Efficient feature selection filters for high-dimensional data. Pattern Recognition Letters. ISSN: 0167-8655. Vol. 33, N.º 13 (2012), pp. 1794-1804.por
dc.identifier.doi10.1016/j.patrec.2012.05.019
dc.identifier.issn0167-8655
dc.identifier.urihttp://hdl.handle.net/10400.21/5081
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevier Science BVpor
dc.relationPolytechnic Institute of Lisbon - SFRH/PROTEC/67605/2010
dc.relationFCT project - PEst-OE/EEI/LA0008/2011
dc.subjectFeature selectionpor
dc.subjectFilterspor
dc.subjectDispersion measurespor
dc.subjectSimilarity measurespor
dc.subjectHigh-dimensional datapor
dc.titleEfficient feature selection filters for high-dimensional datapor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceAmsterdam
oaire.citation.endPage1804por
oaire.citation.issue13por
oaire.citation.startPage1794por
oaire.citation.titlePattern Recognition Letterspor
oaire.citation.volume33por
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
rcaap.rightsclosedAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication734bfe75-0c68-4cdf-8a87-2aef3564f5bd
relation.isAuthorOfPublication.latestForDiscovery734bfe75-0c68-4cdf-8a87-2aef3564f5bd

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