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Classification and combining models

dc.contributor.authorMarques, Anabela
dc.contributor.authorFerreira, Ana Sousa
dc.contributor.authorCardoso, Margarida
dc.date.accessioned2015-01-02T10:11:51Z
dc.date.available2015-01-02T10:11:51Z
dc.date.issued2010-06
dc.descriptionTrabalho apresentado em SMTDA 2010: Stochastic Modeling Techniques and Data Analysis International Conference, Chania, Crete, Greece, 8-11 june 2010por
dc.description.abstractIn the context of Discrete Discriminant Analysis (DDA) the idea of combining models is present in a growing number of papers aiming to obtain more robust and more stable models. This seems to be a promising approach since it is known that different DDA models perform differently on different subjects. Furthermore, the idea of combining models is particularly relevant when the groups are not well separeted, which often occurs in practice. Recently, we proposed a new DDA approach which is based on a linear combination of the First-order Independence Model (FOIM) and the Dependence Trees Model (DTM). In the present work we apply this new approach to classify consumers of a Portuguese cultural institution. We specifically focus on the performance of alternative models' combinations assessing the error rate and the Huberty index in a test sample. We use the R software for the algorithms' implementation and evaluation.en
dc.identifier.citationIn SMTDA2010 Proceedingsen
dc.identifier.urihttp://hdl.handle.net/10400.26/7396
dc.language.isoengen
dc.publisherSMTDAen
dc.relation.publisherversionhttp://www.smtda.net/smtda2010proceedings.htmlpor
dc.subjectCombining modelsen
dc.subjectDependence Trees Modelsen
dc.subjectDiscrete Discriminant Analysisen
dc.subjectFirst-order Independence Modelen
dc.titleClassification and combining modelsen
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceChania, Creta, Gréciapor
oaire.citation.titleSMTDA 2010: Stochastic Modeling Techniques and Data Analysis International Conferenceen
rcaap.rightsopenAccessen
rcaap.typeconferenceObjecten

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