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Modèle crédibiliste pour l'échantillonnage en apprentissage actif

Abstract : In machine learning, training a classifier on large dataset requires an important amount of labels which is expensive in terms of human resources and money. A possible solution to this problem is to use crowdsourcing in order to label the data. Although, non-expert people do not always have the knowledge to do their work correctly, leading to introducing errors in labeling. Active learning offers a solution to the labeling cost by making the classifier choose the data it wants to label in order to reach good performance with fewer labels. By combining active learning and belief functions, it becomes possible to model the errors and uncertainty in labels. We propose a new sampling method implying belief entropies.
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Conference papers
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Contributor : Daniel Zhu Connect in order to contact the contributor
Submitted on : Friday, August 27, 2021 - 12:52:50 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Sunday, November 28, 2021 - 6:02:13 PM


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  • HAL Id : hal-03327140, version 1


Daniel Zhu, Arnaud Martin, Jean-Christophe Dubois, Yolande Le Gall, Vincent Lemaire. Modèle crédibiliste pour l'échantillonnage en apprentissage actif. Rencontres francophones sur la logique floue et ses applications, Oct 2021, Paris, France. ⟨hal-03327140⟩



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