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Using ensembles for problems with characterizable changes in data distribution: A case study on quantification

dc.contributor.authorPérez Gallego, Pablo José 
dc.contributor.authorQuevedo Pérez, José Ramón 
dc.contributor.authorCoz Velasco, Juan José del
dc.identifier.citationInformation Fusion, 34, p. 87–100 (2017); doi:10.1016/j.inffus.2016.07.001
dc.description.abstractEnsemble methods are widely applied to supervised learning tasks. Based on a simple strategy they often achieve good performance, especially when the single models comprising the ensemble are diverse. Diversity can be introduced into the ensemble by creating different training samples for each model. In that case, each model is trained with a data distribution that may be different from the original training set distribution. Following that idea, this paper analyzes the hypothesis that ensembles can be especially appropriate in problems that: (i) suffer from distribution changes, (ii) it is possible to characterize those changes beforehand. The idea consists in generating different training samples based on the expected distribution changes, and to train one model with each of them. As a case study, we shall focus on binary quantification problems, introducing ensembles versions for two well-known quantification algorithms. Experimental results show that these ensemble adaptations outperform the original counterpart algorithms, even when trivial aggregation rules are usedspa
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). Juan Jos e del Coz is also supported by the Fulbright Commission and the Salvador de Madariaga Program, grant PRX15/00607spa
dc.format.extentp. 87–100spa
dc.relation.ispartofInformation Fusion, 34spa
dc.rights© 2017 Elsevier*
dc.rightsCC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
dc.subjectDistribution changesspa
dc.titleUsing ensembles for problems with characterizable changes in data distribution: A case study on quantificationspa
dc.typejournal articlespa
dc.rights.accessRightsopen accessspa

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