Evidential uncertainty sampling for active learning
This work addresses active learning challenges for machine learning practitioners by offering incremental improvements in uncertainty sampling efficiency.
The paper tackled the problem of simplifying uncertainty sampling in active learning by addressing label uncertainty and proposing two strategies based on belief functions, resulting in a method that outperforms standard uncertainty sampling in experiments.
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, 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.