Evidential uncertainties on rich labels for active learning
Arthur Hoarau Vincent Lemaire Arnaud Martin Jean-Christophe Dubois Yolande Le Gall
Recent research in active learning, and more precisely in uncertainty
sampling, has focused on the decomposition of model uncertainty into reducible
and irreducible uncertainties. In this paper, we propose to simplify the
computational phase and remove the dependence on observations, but more
importantly to take into account the uncertainty already present in the labels,
\emph{i.e.} the uncertainty of the oracles. Two strategies are proposed,
sampling by Klir uncertainty, which addresses the exploration-exploitation
problem, and sampling by evidential epistemic uncertainty, which extends the
reducible uncertainty to the evidential framework, both using the theory of
belief functions.