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Customized crowds and active learning to improve classification

2013-12Artigo científico Acesso restrito
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datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
dc.contributor.authorCosta, Joana
dc.contributor.authorSilva, Catarina
dc.contributor.authorAntunes, Mário
dc.contributor.authorRibeiro, Bernardete
dc.date.accessioned2026-03-09T16:18:50Z
dc.date.available2026-03-09T16:18:50Z
dc.date.issued2013-12
dc.description.abstractTraditional classification algorithms can be limited in their performance when a specific user is targeted. User preferences, e.g. in recommendation systems, constitute a challenge for learning algorithms. Additionally, in recent years user’s interaction through crowdsourcing has drawn significant interest, although its use in learning settings is still underused. In this work we focus on an active strategy that uses crowd-based non-expert information to appropriately tackle the problem of capturing the drift between user preferences in a recommendation system. The proposed method combines two main ideas: to apply active strategies for adaptation to each user; to implement crowdsourcing to avoid excessive user feedback. A similitude technique is put forward to optimize the choice of the more appropriate similitude-wise crowd, under the guidance of basic user feedback. The proposed active learning framework allows non-experts classification performed by crowds to be used to define the user profile, mitigating the labeling effort normally requested to the user. The framework is designed to be generic and suitable to be applied to different scenarios, whilst customizable for each specific user. A case study on humor classification scenario is used to demonstrate experimentally that the approach can improve baseline active results.eng
dc.description.sponsorshipThis work is partly funded by the ERDF, through the COMPETE Programme, by FCT within project PEst-C/EEI/LA0014/2011 and by iCIS (CENTRO-07-ST24-FEDER-002003).
dc.identifier.citationJoana Costa, Catarina Silva, Mário Antunes, Bernardete Ribeiro, Customized crowds and active learning to improve classification, Expert Systems with Applications, Volume 40, Issue 18, 2013, Pages 7212-7219, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2013.06.072.
dc.identifier.doi10.1016/j.eswa.2013.06.072
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/10400.8/15817
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S0957417413004715?via%3Dihub
dc.relation.ispartofExpert Systems with Applications
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCrowdsourcing
dc.subjectActive learning
dc.subjectClassification
dc.titleCustomized crowds and active learning to improve classificationeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage7219
oaire.citation.issue18
oaire.citation.startPage7212
oaire.citation.titleExpert Systems with Applications
oaire.citation.volume40
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCosta
person.familyNameSilva
person.familyNameAntunes
person.givenNameJoana
person.givenNameCatarina
person.givenNameMário
person.identifierR-000-NX4
person.identifier.ciencia-id1B19-3DDC-BE75
person.identifier.ciencia-idAF10-7EDD-5153
person.identifier.orcid0000-0002-4053-5718
person.identifier.orcid0000-0002-5656-0061
person.identifier.orcid0000-0003-3448-6726
person.identifier.scopus-author-id25930820200
relation.isAuthorOfPublication23d200dc-1a81-4bd9-9a4a-0efc28af6ce4
relation.isAuthorOfPublicationee28e079-5ca7-4842-9094-372c40f75c38
relation.isAuthorOfPublicatione3e87fb0-d1d6-44c3-985d-920a5560f8c1
relation.isAuthorOfPublication.latestForDiscovery23d200dc-1a81-4bd9-9a4a-0efc28af6ce4

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