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dc.creatorJoão Mendes Moreira
dc.creatorAlípio Mário Jorge
dc.creatorCarlos Soares
dc.creatorJorge Freire de Sousa
dc.description.abstractIntegration methods for ensemble learning can use two different approaches: combination or selection. The combination approach (also called fusion) consists on the combination of the predictions obtained by different models in the ensemble to obtain the final ensemble predication. The selection approach selects one (or more) models from the ensemble according to the prediction performance of these models on similar data from the validation set. Usually, the method to select similar data is the k-nearest neighbors with the Euclidean distance. In this paper we discuss other approaches to obtain similar data for the regression problem. We show that using similarity measures according to the target values improves results. We also show that selecting dynamically several models for the prediction task increases prediction accuracy comparing to the selection of just one model.
dc.relation.ispartofMachine Learning and Data Mining in Pattern Recognition
dc.subjectInteligência artificial, Ciências da computação e da informação
dc.subjectArtificial intelligence, Computer and information sciences
dc.titleEnsemble learning: A study on different variants of the dynamic selection approach
dc.typeArtigo em Livro de Atas de Conferência Internacional
dc.contributor.uportoFaculdade de Economia
dc.contributor.uportoFaculdade de Engenharia
dc.contributor.uportoFaculdade de Ciências
dc.subject.fosCiências exactas e naturais::Ciências da computação e da informação
dc.subject.fosNatural sciences::Computer and information sciences
Appears in Collections:FCUP - Artigo em Livro de Atas de Conferência Internacional
FEP - Artigo em Livro de Atas de Conferência Internacional
FEUP - Artigo em Livro de Atas de Conferência Internacional

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