Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.

翻译：在线主动学习是机器学习中一种旨在从数据流中选择最具信息量的数据点进行标注的范式。近年来，特别是针对仅有未标注形式数据的实际应用场景，最小化收集标注观测成本的问题受到了广泛关注。对每个观测进行标注既耗时又昂贵，这使得获取大量标注数据变得困难。为克服这一问题，过去数十年间提出了许多主动学习策略，旨在通过选择最具信息量的观测进行标注来提升机器学习模型的性能。这些方法大致可分为两类：静态池基主动学习和流基主动学习。池基主动学习涉及从封闭的未标注数据池中选择观测子集，这一方向已成为众多综述与文献回顾的焦点。然而，随着数据流可用性的日益增长，专注于在线主动学习的方法数量不断增加，这类方法需在数据流到达时持续选择并标注观测。本文旨在概述近年来从数据流中实时选择最具信息量观测的最新方法。我们回顾了已提出的各类技术，并讨论其优势与局限性，以及该研究领域存在的挑战与机遇。