Epistemic Uncertainty Sampling

TL;DR

This paper introduces epistemic uncertainty sampling in active learning, emphasizing the importance of distinguishing between reducible and irreducible uncertainties to improve query strategies.

Contribution

It proposes a novel approach to measure and utilize epistemic uncertainty in active learning, demonstrating its potential advantages over traditional probabilistic uncertainty measures.

Findings

Epistemic uncertainty sampling outperforms traditional methods in experiments.

Distinguishing between epistemic and aleatoric uncertainty improves active learning efficiency.

The approach shows promising results in reducing labeling efforts.

Abstract

Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are almost exclusively of a probabilistic nature. In this paper, we advocate a distinction between two different types of uncertainty, referred to as epistemic and aleatoric, in the context of active learning. Roughly speaking, these notions capture the reducible and the irreducible part of the total uncertainty in a prediction, respectively. We conjecture that, in uncertainty sampling, the usefulness of an instance is better reflected by its epistemic than by its aleatoric uncertainty. This leads us to suggest the principle of "epistemic uncertainty sampling", which we instantiate by means of a concrete approach for measuring epistemic and aleatoric uncertainty. In experimental studies, epistemic uncertainty sampling does indeed show promising performance.