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Dynamic ensemble selection for quantification tasks

dc.contributor.authorPérez Gallego, Pablo José 
dc.contributor.authorCastaño Gutiérrez, Alberto 
dc.contributor.authorQuevedo Pérez, José Ramón 
dc.contributor.authorCoz Velasco, Juan José del
dc.identifier.citationInformation Fusion, 45, p. 1-15 (2018); doi:10.1016/j.inffus.2018.01.001
dc.description.abstractEnsembles are among the most effective and successful methods for almost all supervised tasks. Not long ago, an ensemble approach has been proposed for quantification learning The idea of such method is to exploit the prior knowledge about quantification tasks, building ensembles in which diversity is achieved by training each model with a different distribution. These training samples are generated taking into account the expected drift in class distribution. This paper extends this method proposing three new quantifier selection criteria particularly devised for quantification problems, where two of them are defined for dynamic ensemble selection. The experiments demonstrate that, in many cases, these selection functions outperform straightforward approaches, like averaging all models and using quantification accuracy to prune the ensemble. Moreover, the results show that performance heavily depends on the combination of the base quantification algorithm and the selection measurespa
dc.description.sponsorshipThis research has been funded by MINECO (the Spanish Ministerio de Econom a y Competitividad) and FEDER (Fondo Europeo de Desarrollo Regional), grant TIN2015-65069-C2-2-R (MINECO/FEDER)spa
dc.relation.ispartofInformation Fusion, 45spa
dc.rights© 2018 Elsevier
dc.rightsCC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
dc.subjectDynamic Ensemble Selectionspa
dc.titleDynamic ensemble selection for quantification tasksspa
dc.typejournal articlespa
dc.rights.accessRightsopen accessspa

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