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Feature Discretization with Relevance and Mutual Information Criteria

2015Conference object Restricted access
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dc.contributor.authorJ. Ferreira, Artur
dc.contributor.authorFigueiredo, Mário A. T.
dc.date.accessioned2016-04-21T11:14:57Z
dc.date.available2016-04-21T11:14:57Z
dc.date.issued2015
dc.description.abstractFeature discretization (FD) techniques often yield adequate and compact representations of the data, suitable for machine learning and pattern recognition problems. These representations usually decrease the training time, yielding higher classification accuracy while allowing for humans to better understand and visualize the data, as compared to the use of the original features. This paper proposes two new FD techniques. The first one is based on the well-known Linde-Buzo-Gray quantization algorithm, coupled with a relevance criterion, being able perform unsupervised, supervised, or semi-supervised discretization. The second technique works in supervised mode, being based on the maximization of the mutual information between each discrete feature and the class label. Our experimental results on standard benchmark datasets show that these techniques scale up to high-dimensional data, attaining in many cases better accuracy than existing unsupervised and supervised FD approaches, while using fewer discretization intervals.pt_PT
dc.identifier.citationFERREIRA, Artur J.; FIGUEIREDO, Mário A. T. - Feature Discretization with Relevance and Mutual Information Criteria. Pattern Recognition Applications and Methods. Barcelona: SPRINGER-VERLAG BERLIN, 2015. ISBN.978-3-319-12610-4. Vol. 318, pp. 101-118pt_PT
dc.identifier.doi10.1007/978-3-319-12610-4_7pt_PT
dc.identifier.isbn978-3-319-12610-4
dc.identifier.isbn978-3-319-12609-8
dc.identifier.issn2194-5357
dc.identifier.urihttp://hdl.handle.net/10400.21/6073
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSPRINGER-VERLAG BERLINpt_PT
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007%2F978-3-319-12610-4_7pt_PT
dc.subjectClassificationpt_PT
dc.subjectFeature discretizationpt_PT
dc.subjectLinde-Buzo-Graypt_PT
dc.subjectMutual informationpt_PT
dc.subjectQuantizationpt_PT
dc.subjectRelevancept_PT
dc.subjectSupervised learningpt_PT
dc.titleFeature Discretization with Relevance and Mutual Information Criteriapt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceBarcelona, SPAINpt_PT
oaire.citation.endPage118pt_PT
oaire.citation.startPage101pt_PT
oaire.citation.title2nd International Conference on Pattern Recognition Applications and Methods (ICPRAM)pt_PT
oaire.citation.volume318pt_PT
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.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication734bfe75-0c68-4cdf-8a87-2aef3564f5bd
relation.isAuthorOfPublication.latestForDiscovery734bfe75-0c68-4cdf-8a87-2aef3564f5bd

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