A popular strategy for active learning is to specifically target a reduction in epistemic uncertainty, since aleatoric uncertainty is often considered as being intrinsic to the system of interest and therefore not reducible. Yet, distinguishing these two types of uncertainty remains challenging and there is no single strategy that consistently outperforms the others. We propose to use a particular combination of probability and possibility theories, with the aim of using the latter to specifically represent epistemic uncertainty, and we show how this combination leads to new active learning strategies that have desirable properties. In order to demonstrate the efficiency of these strategies in non-trivial settings, we introduce the notion of a possibilistic Gaussian process (GP) and consider GP-based multiclass and binary classification problems, for which the proposed methods display a strong performance for both simulated and real datasets.

翻译：主动学习的一种常用策略是专门针对认知不确定性的降低，因为偶然不确定性通常被视为目标系统固有的、不可约简的部分。然而，区分这两种不确定性类型仍然具有挑战性，目前尚无单一策略能持续优于其他方法。我们提出采用概率论与可能性理论的特定组合，旨在利用后者专门表征认知不确定性，并展示了这种组合如何产生具有理想性质的新型主动学习策略。为了在非平凡场景中验证这些策略的有效性，我们引入了可能性高斯过程（GP）的概念，并考虑了基于GP的多类与二分类问题。实验表明，所提方法在模拟和真实数据集上均表现出优越性能。