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Use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10651/46050

Título : Dynamic ensemble selection for quantification tasks
Autor(es) y otros: Pérez Gallego, Pablo José
Castaño Gutiérrez, Alberto
Quevedo Pérez, José Ramón
Coz Velasco, Juan José del
Palabras clave: Quantification
Ensembles
Dynamic Ensemble Selection
Fecha de publicación : 2018
Editorial : Elsevier
Versión del editor: http://dx.doi.org/10.1016/j.inffus.2018.01.001
Citación : Information Fusion, 45, p. 1-15 (2018); doi:10.1016/j.inffus.2018.01.001
Resumen : Ensembles 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 measure
Fecha fin embargo: 2020-01-10
URI : http://hdl.handle.net/10651/46050
ISSN : 1566-2535
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