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Dynamic ensemble selection for quantification tasks
dc.contributor.author | Pérez Gallego, Pablo José | |
dc.contributor.author | Castaño Gutiérrez, Alberto | |
dc.contributor.author | Quevedo Pérez, José Ramón | |
dc.contributor.author | Coz Velasco, Juan José del | |
dc.date.accessioned | 2018-02-21T11:59:58Z | |
dc.date.available | 2018-02-21T11:59:58Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Information Fusion, 45, p. 1-15 (2018); doi:10.1016/j.inffus.2018.01.001 | |
dc.identifier.issn | 1566-2535 | |
dc.identifier.uri | http://hdl.handle.net/10651/46050 | |
dc.description.abstract | 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 | spa |
dc.description.sponsorship | This 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.language.iso | eng | spa |
dc.publisher | Elsevier | spa |
dc.relation.ispartof | Information Fusion, 45 | spa |
dc.rights | © 2018 Elsevier | |
dc.rights | CC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Quantification | spa |
dc.subject | Ensembles | spa |
dc.subject | Dynamic Ensemble Selection | spa |
dc.title | Dynamic ensemble selection for quantification tasks | spa |
dc.type | journal article | spa |
dc.identifier.doi | 10.1016/j.inffus.2018.01.001 | |
dc.relation.projectID | MINECO-FEDER/TIN2015-65069-C2-2-R | spa |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.inffus.2018.01.001 | spa |
dc.rights.accessRights | open access | spa |
dc.type.hasVersion | AM |
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