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Article Dans Une Revue Machine Learning Année : 2024

Evidential uncertainty sampling for active learning

Vincent Lemaire
Yolande Le Gall
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Jean-Christophe Dubois
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Arnaud Martin

Résumé

Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and irreducible uncertainties. In this paper, the aim is to simplify the computational process while eliminating the dependence on observations. Crucially, the inherent uncertainty in the labels is considered, i.e. the uncertainty of the oracles. Two strategies are proposed, sampling by Klir uncertainty, which tackles the exploration-exploitation dilemma, and sampling by evidential epistemic uncertainty, which extends the concept of reducible uncertainty within the evidential framework, both using the theory of belief functions. Experimental results in active learning demonstrate that our proposed method can outperform uncertainty sampling.
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Dates et versions

hal-04623676 , version 1 (25-06-2024)

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  • HAL Id : hal-04623676 , version 1

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Arthur Hoarau, Vincent Lemaire, Yolande Le Gall, Jean-Christophe Dubois, Arnaud Martin. Evidential uncertainty sampling for active learning. Machine Learning, In press. ⟨hal-04623676⟩
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