Skip navigation
Logo
User training | Reference and search service

Library catalog

Retrievo
EDS
b-on
More
resources
Content aggregators
Please use this identifier to cite or link to this item:

acessibilidade

http://hdl.handle.net/10071/12288
Full metadata record
acessibilidade
DC FieldValueLanguage
dc.contributor.authorMarques, A.-
dc.contributor.authorFerreira, A. S.-
dc.contributor.authorCardoso, M. G. M. S.-
dc.date.accessioned2016-12-16T14:08:10Z-
dc.date.available2016-12-16T14:08:10Z-
dc.date.issued2016-
dc.identifier.issn1755-8050-
dc.identifier.urihttp://hdl.handle.net/10071/12288-
dc.description.abstractWhen conducting discrete discriminant analysis, alternative models provide different levels of predictive accuracy which has encouraged the research in combined models. This research seems to be specially promising when small or moderate sized samples are considered, which often occurs in practice. In this work we evaluate the performance of a linear combination of two discrete discriminant analysis models: the first-order independence model and the dependence trees model. The proposed methodology also uses a hierarchical coupling model when addressing multi-class classification problems, decomposing the multi-class problems into several bi-class problems, using a binary tree structure. The analysis is based both on simulated and real datasets. Results of the proposed approach are compared with those obtained by random forests, being generally more accurate. Measures of precision regarding a training set, a test set and cross-validation are presented. The R software is used for the algorithms' implementation.eng
dc.language.isoeng-
dc.publisherInderscience-
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147442/PT-
dc.rightsopenAccesspor
dc.subjectCombining modelseng
dc.subjectDDAeng
dc.subjectDependence trees modeleng
dc.subjectDiscrete discriminant analysiseng
dc.subjectDTMeng
dc.subjectFirst-order independence modeleng
dc.subjectFOIMeng
dc.subjectHierarchical coupling modeleng
dc.subjectHIERMeng
dc.subjectRandom foresteng
dc.subjectRFeng
dc.titleCombining models in discrete discriminant analysiseng
dc.typearticle-
dc.pagination143 - 160-
dc.publicationstatusPublicadopor
dc.peerreviewedyes-
dc.journalInternational Journal of Data Analysis Techniques and Strategies-
dc.distributionInternacionalpor
dc.volume8-
dc.number2-
degois.publication.firstPage143-
degois.publication.lastPage160-
degois.publication.issue2-
degois.publication.titleCombining models in discrete discriminant analysiseng
dc.date.updated2019-04-22T12:50:11Z-
dc.description.versioninfo:eu-repo/semantics/submittedVersion-
dc.identifier.doi10.1504/IJDATS.2016.077483-
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Matemáticaspor
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informaçãopor
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-29254-
iscte.alternateIdentifiers.scopus2-s2.0-84989844343-
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

Files in This Item:
acessibilidade
File Description SizeFormat 
Publicação_IJDAT_2016_VersãoInicial.pdfPré-print271.87 kBAdobe PDFView/Open


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Currículo DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.