Show simple item record

dc.contributor.authorCabrera-Bean, Margarita
dc.contributor.authorPagès Zamora, Alba Maria
dc.contributor.authorDiaz Vilor, Carles
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2019-06-21T16:18:54Z
dc.date.available2019-06-21T16:18:54Z
dc.date.issued2019-07-01
dc.identifier.citationCabrera-Bean, M.; Pagès-Zamora, A.; Diaz, C. Unsupervised ensemble classification with correlated decision agents. "IEEE signal processing letters", 1 Juliol 2019, vol. 26, núm. 7, p. 1085-1089.
dc.identifier.issn1070-9908
dc.identifier.urihttp://hdl.handle.net/2117/134945
dc.description© 2019 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.abstractDecision-making procedures when a set of individual binary labels is processed to produce a unique joint decision can be approached modeling the individual labels as multivariate independent Bernoulli random variables. This probabilistic model allows an unsupervised solution using EM-based algorithms, which basically estimate the distribution model parameters and take a joint decision using a Maximum a Posteriori criterion. These methods usually assume that individual decision agents are conditionally independent, an assumption that might not hold in practical setups. Therefore, in this work we formulate and solve the decision-making problem using an EM-based approach but assuming correlated decision agents. Improved performance is obtained on synthetic and real datasets, compared to classical and state-of-the-art algorithms.
dc.format.extent5 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
dc.subject.lcshSignal processing
dc.subject.otherCorrelated Bernoulli distribution
dc.subject.otherCrowdsourcing
dc.subject.otherUnsupervised ensemble learning
dc.subject.otherCorrelated decision agents
dc.titleUnsupervised ensemble classification with correlated decision agents
dc.typeArticle
dc.subject.lemacTractament del senyal
dc.contributor.groupUniversitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
dc.identifier.doi10.1109/LSP.2019.2918945
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8721508
dc.rights.accessOpen Access
drac.iddocument25183063
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2016-77148-C2-1-R
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2016-75067-C4-2-R
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/PRI2017-2019/2017 SGR 578
upcommons.citation.authorCabrera-Bean, Margarita; Pagès-Zamora, A.; Diaz, C.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameIEEE signal processing letters
upcommons.citation.volume26
upcommons.citation.number7
upcommons.citation.startingPage1085
upcommons.citation.endingPage1089


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder