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Learning from unequally reliable blind ensembles of classifiers
dc.contributor.author | Traganitis, Panagiotis |
dc.contributor.author | Pagès Zamora, Alba Maria |
dc.contributor.author | Giannakis, Georgios B. |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2018-06-05T15:16:58Z |
dc.date.available | 2018-06-05T15:16:58Z |
dc.date.issued | 2017 |
dc.identifier.citation | Traganitis, P. A., Pagès-Zamora, A., Giannakis, G.B. Learning from unequally reliable blind ensembles of classifiers. A: IEEE Global Conference on Signal and Information Processingg. "2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017): Montreal, Quebec, Canada: 14-16 November 2017". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 106-110. |
dc.identifier.isbn | 978-1-5090-5991-1 |
dc.identifier.uri | http://hdl.handle.net/2117/117837 |
dc.description | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
dc.description.abstract | The rising interest in pattern recognition and data analytics has spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm has its strengths and weaknesses, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims to create a high- performance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a simple blind scheme for learning from ensembles of classifiers, using joint matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. Performance is evaluated on synthetic and real datasets. |
dc.format.extent | 5 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.lcsh | Artificial intelligence -- Educational applications |
dc.subject.other | Ensemble learning |
dc.subject.other | Multi-class classification |
dc.subject.other | Unsupervised |
dc.title | Learning from unequally reliable blind ensembles of classifiers |
dc.type | Conference report |
dc.subject.lemac | Intel·ligència artificial -- Aplicacions a l'educació |
dc.contributor.group | Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions |
dc.identifier.doi | 10.1109/GlobalSIP.2017.8308613 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8308613/ |
dc.rights.access | Open Access |
local.identifier.drac | 22965695 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TEC2013-41315-R/ES/TECNICAS DISTRIBUIDAS PARA LA GESTION Y OPERACION DE REDES DE COMUNICACIONES CELULARES INALAMBRICAS, DE SENSORES Y DE LA RED ELECTRICA INTELIGENTE/ |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TEC2015-69648-REDC/ES/RED COMONSENS/ |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/2PE/TEC2016-75067-C4-2-R-CARMEN |
local.citation.author | Traganitis, P. A.; Pagès-Zamora, A.; Giannakis, G.B. |
local.citation.contributor | IEEE Global Conference on Signal and Information Processingg |
local.citation.publicationName | 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017): Montreal, Quebec, Canada: 14-16 November 2017 |
local.citation.startingPage | 106 |
local.citation.endingPage | 110 |