Introduction

A Survey on Ensemble Learning for Data Stream Classification provides a comprehensive overview of how ensemble methods have been adapted to meet the challenges of real‑time, continuously evolving data streams. As data arrives rapidly and underlying patterns shift over time, traditional batch‑learning approaches struggle to maintain accuracy. This survey examines the landscape of ensemble‑based techniques designed specifically for streaming environments, outlining how they address issues such as concept drift, limited memory, and the need for fast, incremental updates.

Outcomes

The article synthesizes the major families of ensemble methods used in data‑stream classification, including online bagging and boosting, adaptive ensembles, hybrid approaches, and drift‑aware frameworks. It highlights the strengths and limitations of each technique, explaining how they manage evolving data distributions, maintain diversity among base learners, and balance stability with adaptability. The survey also identifies emerging trends, such as resource‑efficient ensembles, concept‑drift detection strategies, and the integration of deep learning. This offers a roadmap for future research. Overall, the work provides a clear, structured understanding of how ensemble learning has become a cornerstone of effective data‑stream classification.

This publication includes contributions from Albert, Jesse, and Geoff, all of whom play key roles in advancing TAIAO’s mission to develop adaptive, scalable machine‑learning methods for complex, continuously evolving environmental and ecological data streams.

Bifet, Albert, Gianmarco De Francisci Morales, Jesse Read, and Geoff Holmes. “A Survey on Ensemble Learning for Data Stream Classification.” ACM Computing Surveys, vol. 54, no. 2, 2021, pp. 1–36.