Active learning for data streams - a survey

Aug 2, 2026

Uploaded by Logic

Online active learning is a machine learning paradigm that selects the most informative data points to label from a continuous data stream. It addresses the high cost and time required for obtaining labeled observations, which limits supervised training in real-world scenarios where data is initially unlabeled. The review distinguishes static pool-based methods from stream-based online approaches, then synthesizes recent query strategies. It analyzes strengths and limitations, and highlights evaluation practices, challenges, and future research opportunities in the field.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages34

Reading Time1 h 26 min

AccessFree to download

Introduction

Active learning motivation and cost trade-offs

Pool-based vs online active learning

Preliminaries on active learning

Supervised learning and labeling context

Review structure

Classification of online approaches

Evaluation strategies

Summary, applications, and future directions

A survey on online active learning Davide Cacciarelli 1, 2 Murat Kulahci 1, 3 1 Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark 2 Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway 3 Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, Luleå, Sweden Abstract 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 the context of online active learning. We review the various techniques that have been proposed and discuss their strengths and limitations, as well asthe challenges and opportunities that exist in this area of research. Our review aims to provide a comprehensive and up-to-date overview of the field and to highlight directions for future work. Keywords: active learning, data streams, online learning, unlabeled data, query strategy, literature review. 1 Introduction The deployment of machine learning models in real-world applications is often reliant on the availability of significant amounts of annotated data. While recent advancements in sensor technology have facilitated the collection of larger amounts of data, this data is not always labeled and ready for use in training models. Indeed, the process of obtaining labeled observations for supervised learning models can be cost-prohibitive and timeconsuming, as it often requires quality inspections or manual annotation. In such cases, active learning proves tobe a valuable strategy to identify the most informative data points for use in training, thereby reducing the overall cost of labeling and improving the performance of the model. Over the years, a plethora of active learning approaches have been proposed in the literature, each with its own benefits and limitations. These approaches seek to strike a balance between the cost of labeling and the quality of the model by selectively choosing the most informative observations for querying. By carefully selecting the most informative observations, active learning helps to minimize the amount of labeled data required and streamlines the learning process, contributing to its overall efficiency. While several surveys have been published on pool-based active learning [1–5], which involves selecting a fixed set of observations from a pool of unlabeled data, the dynamic and sequential nature of many real-world problems often renders these approaches impractical. This has led to growing interest in the online variant of active learning, which involves continuously selecting and labeling observations as they arrive in a stream, allowing for real-time ada

FAQs

What problem does online active learning address?

It aims to reduce the cost and time of labeling by selecting the most informative observations from an unlabeled data stream for real-time model improvement.

How does pool-based active learning differ from stream-based online active learning?

Pool-based methods select a subset from a closed pool of unlabeled data, while stream-based online active learning continuously selects and labels observations as they arrive.

What does the survey focus on regarding online active learning?

It provides an overview of recent query strategies, reviews techniques with strengths and limitations, and discusses evaluation strategies and real-world applications, along with future research directions.

Aug 2, 2026

Uploaded by Logic