Imperfect Labels with Belief Functions for Active Learning - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Imperfect Labels with Belief Functions for Active Learning

Résumé

Classification is used to predict classes by extracting information from labeled data. But sometimes the collected data is imperfect, as in crowdsourcing where users have partial knowledge and may answer with uncertainty or imprecision. This paper offers a way to deal with uncertain and imprecise labeled data using Dempster-Shafer theory and active learning. An evidential version of K-NN that classifies a new example by observing its neighbors was earlier introduced. We propose to couple this approach with active learning, where the model uses only a fraction of the labeled data, and to compare it with non-evidential models. A new computable parameter for EK-NN is introduced, allowing the model to be both compatible with imperfectly labeled data and equivalent to its first version in the case of perfectly labeled data. This method increases the complexity but provides a way to work with imperfectly labeled data with efficient results and reduced labeling costs when coupled with active learning. We have conducted tests on real data imperfectly labeled during crowdsourcing campaigns.
Fichier principal
Vignette du fichier
Belief2022.pdf (328.07 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03817218 , version 1 (17-10-2022)

Identifiants

Citer

Arthur Hoarau, Arnaud Martin, Jean-Christophe Dubois, Yolande Le Gall. Imperfect Labels with Belief Functions for Active Learning. Belief Functions: Theory and Applications, Oct 2022, Paris, France. pp.44-53, ⟨10.1007/978-3-031-17801-6_5⟩. ⟨hal-03817218⟩
62 Consultations
77 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More