Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/27286
DC FieldValueLanguage
dc.contributor.authorCosta, Joana-
dc.contributor.authorSilva, Catarina-
dc.contributor.authorAntunes, Mário-
dc.contributor.authorRibeiro, Bernardete-
dc.date.accessioned2014-10-15T11:06:41Z-
dc.date.available2014-10-15T11:06:41Z-
dc.date.issued2013-12-15-
dc.identifier.citationCOSTA, Joana [et. al] - Customized crowds and active learning to improve classification. "Expert Systems with Applications". ISSN 0957-4174. Vol. 40 Nº. 18 (2013) p. 7212-7219por
dc.identifier.issn0957-4174-
dc.identifier.urihttps://hdl.handle.net/10316/27286-
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.por
dc.language.isoengpor
dc.publisherElsevierpor
dc.rightsopenAccesspor
dc.subjectCrowdsourcingpor
dc.subjectActive learningpor
dc.subjectClassificationpor
dc.titleCustomized crowds and active learning to improve classificationpor
dc.typearticlepor
degois.publication.firstPage7212por
degois.publication.lastPage7219por
degois.publication.issue18por
degois.publication.titleExpert Systems with Applicationspor
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0957417413004715por
dc.peerreviewedYespor
dc.identifier.doi10.1016/j.eswa.2013.06.072-
degois.publication.volume40por
uc.controloAutoridadeSim-
item.openairetypearticle-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextCom Texto completo-
crisitem.author.researchunitCenter for Research in Neuropsychology and Cognitive Behavioral Intervention-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0002-0942-936X-
crisitem.author.orcid0000-0002-5656-0061-
crisitem.author.orcid0000-0002-9770-7672-
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
FCTUC Eng.Informática - Artigos em Revistas Internacionais
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