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On the improvement of feature selection techniques: the fitness filter

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dc.contributor.authorJ. Ferreira, Artur
dc.contributor.authorFigueiredo, Mario
dc.date.accessioned2021-10-18T13:17:22Z
dc.date.available2021-10-18T13:17:22Z
dc.date.issued2021
dc.description.abstractThe need for feature selection (FS) techniques is central in many machine learning and pattern recognition problems. FS is a vast research field and therefore we now have many FS techniques proposed in the literature, applied in the context of quite different problems. Some of these FS techniques follow the relevance-redundancy (RR) framework to select the best subset of features. In this paper, we propose a supervised filter FS technique, named as fitness filter, that follows the RR framework and uses data discretization. This technique can be used directly on low or medium dimensional data or it can be applied as a post-processing technique to other FS techniques. Specifically, when used as a post-processing technique, it further reduces the dimensionality of the feature space found by common FS techniques and often improves the classification accuracy.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFERREIRA, Artur J.; FIGUEIREDO, Mário A. T. – On the improvement of feature selection techniques: the fitness filter. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM). Vienna, Áustria: Scitepress, 2021. ISBN 978-989-758-486-2. Pp. 365-372pt_PT
dc.identifier.doi10.5220/0010396303650372pt_PT
dc.identifier.isbn978-989-758-486-2
dc.identifier.urihttp://hdl.handle.net/10400.21/13902
dc.language.isoengpt_PT
dc.publisherScitepresspt_PT
dc.subjectMachine learningpt_PT
dc.subjectFeature selectionpt_PT
dc.subjectDimensionality reductionpt_PT
dc.subjectRelevance-redundancypt_PT
dc.subjectClassificationpt_PT
dc.titleOn the improvement of feature selection techniques: the fitness filterpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceFeb 4, 2021 - Feb 6, 2021 - Vienna, Áustriapt_PT
oaire.citation.endPage372pt_PT
oaire.citation.startPage365pt_PT
oaire.citation.title10th International Conference on Pattern Recognition Applications and Methods (ICPRAM)pt_PT
person.familyNameFerreira
person.familyNameFigueiredo
person.givenNameArtur
person.givenNameMario
person.identifier1049438
person.identifier3015485
person.identifier.ciencia-id091A-96FB-A88C
person.identifier.ciencia-idED1E-A787-3569
person.identifier.orcid0000-0002-6508-0932
person.identifier.orcid0000-0002-0970-7745
person.identifier.ridAAL-4377-2020
person.identifier.ridC-5428-2008
person.identifier.scopus-author-id35315359300
person.identifier.scopus-author-id34769730500
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication734bfe75-0c68-4cdf-8a87-2aef3564f5bd
relation.isAuthorOfPublicationd3d068dc-5887-4ecd-bf7f-067ef06e1943
relation.isAuthorOfPublication.latestForDiscovery734bfe75-0c68-4cdf-8a87-2aef3564f5bd

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