Publication:
Recycling weak labels for multiclass classification

Simple item page
dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Machine Learning for Data Science (ML4DS)es
dc.contributor.authorPerello Nieto, Miquel
dc.contributor.authorSantos Rodríguez, Raúl
dc.contributor.authorGarcía García, Dario
dc.contributor.authorCid Sueiro, Jesús
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2023-08-02T11:43:42Z
dc.date.available2023-08-02T11:43:42Z
dc.date.issued2020-08-04
dc.description.abstractThis paper explores the mechanisms to efficiently combine annotations of different quality for multiclass classification datasets, as we argue that it is easier to obtain large collections of weak labels as opposed to true labels. Since labels come from different sources, their annotations may have different degrees of reliability (e.g., noisy labels, supersets of labels, complementary labels or annotations performed by domain experts), and we must make sure that the addition of potentially inaccurate labels does not degrade the performance achieved when using only true labels. For this reason, we consider each group of annotations as being weakly supervised and pose the problem as finding the optimal combination of such collections. We propose an efficient algorithm based on expectation-maximization and show its performance in both synthetic and real-world classification tasks in a variety of weak label scenarios.en
dc.description.sponsorshipThis work was supported by FEDER/Ministry of Science, Innovation and Universities - State Agency of Research [grant TEC2017-83838-R] and also by the EU Horizon-2020 Research and Innovation programme [grant 824988, Project MUSKETEER]. The work of MPN and RSR was supported by the SPHERE Next Steps Project funded by the UK Engineering and Physical Sciences Research Council (EPSRC) [grant EP/R005273/1]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.en
dc.format.extent10
dc.identifier.bibliographicCitationPerello-Nieto, M., Santos-Rodriguez, R., García-García, D., & Cid-Sueiro, J. (2020). Recycling weak labels for multiclass classification. Neurocomputing, 400, 206-215.en
dc.identifier.doihttps://doi.org/10.1016/j.neucom.2020.03.002
dc.identifier.issn0925-2312
dc.identifier.publicationfirstpage206
dc.identifier.publicationlastpage215
dc.identifier.publicationtitleNeurocomputingen
dc.identifier.publicationvolume400
dc.identifier.urihttps://hdl.handle.net/10016/38043
dc.identifier.uxxiAR/0000026969
dc.language.isoengen
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. TEC2017-83838-Res
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/GA-824988
dc.rights© 2020 The Authors.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherClassificationen
dc.subject.otherWeak labelen
dc.subject.otherLoss functionen
dc.subject.otherCost-sensitive learningen
dc.titleRecycling weak labels for multiclass classificationen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Recycling_N_2020.pdf
Size:
1.13 MB
Format:
Adobe Portable Document Format
Download
Collections
DTSC - GPM - Artículos de Revistas
OpenAIRE: Open Access Infrastructure for Research in Europe