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Unsupervised ensemble classification with correlated decision agents
dc.contributor.author | Cabrera-Bean, Margarita |
dc.contributor.author | Pagès Zamora, Alba Maria |
dc.contributor.author | Diaz Vilor, Carles |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2019-06-21T16:18:54Z |
dc.date.available | 2019-06-21T16:18:54Z |
dc.date.issued | 2019-07-01 |
dc.identifier.citation | Cabrera-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.issn | 1070-9908 |
dc.identifier.uri | http://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.abstract | Decision-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.extent | 5 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal |
dc.subject.lcsh | Signal processing |
dc.subject.other | Correlated Bernoulli distribution |
dc.subject.other | Crowdsourcing |
dc.subject.other | Unsupervised ensemble learning |
dc.subject.other | Correlated decision agents |
dc.title | Unsupervised ensemble classification with correlated decision agents |
dc.type | Article |
dc.subject.lemac | Tractament del senyal |
dc.contributor.group | Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions |
dc.identifier.doi | 10.1109/LSP.2019.2918945 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8721508 |
dc.rights.access | Open Access |
local.identifier.drac | 25183063 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/1PE/TEC2016-77148-C2-1-R |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/1PE/TEC2016-75067-C4-2-R |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/PRI2017-2019/2017 SGR 578 |
local.citation.author | Cabrera-Bean, Margarita; Pagès-Zamora, A.; Diaz, C. |
local.citation.publicationName | IEEE signal processing letters |
local.citation.volume | 26 |
local.citation.number | 7 |
local.citation.startingPage | 1085 |
local.citation.endingPage | 1089 |
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