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的一种范式，旨在从数据流中选择信息量最大的数据进行标注。近年来，最小化收集标注观测数据成本的问题引起了广泛关注，特别是在实际应用中数据仅以未标记形式可用的情况下。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。为了克服这一问题，过去几十年中提出了许多主动学习策略，旨在选择信息量最大的观测进行标注，以提高机器学习模型的性能。这些方法大致可分为两类：基于静态池和基于流的主动学习。基于池的主动学习涉及从封闭的未标注数据池中选择一个观测子集，这已成为许多综述和文献综述的焦点。然而，随着数据流的日益普及，专注于在线主动学习的方法数量不断增加，该方法涉及在观测数据流到达时连续选择并标注它们。本文旨在概述最近提出的、从数据流中实时选择信息量最大的观测的方法。我们回顾了各种已提出的技术，讨论了它们的优势和局限性，以及该研究领域中存在的挑战与机遇。