Dynamic Ensemble Active Learning for Drifting Imbalanced Data Streams

Kaixiang Yang

doi:10.1109/tbdata.2026.3720938

Dynamic Ensemble Active Learning for Drifting Imbalanced Data Streams

2026-08-05

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DOI：10.1109/tbdata.2026.3720938

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Concept drift and class imbalance are two critical challenges in data stream analysis, often interrelated in their effects. Concept drift can intensify the impact of class imbalance, while class imbalance can impede the detection and management of concept drift. To address these challenges, this paper introduces a novel semi-supervised classification approach, Dynamic Ensemble Active Learning for Drifting Imbalanced Data Streams (DEAL-DI). DEAL-DI employs a hybrid dynamic labeling strategy that combines an uncertainty threshold with a random selection mechanism. By dynamically adjusting the threshold based on the current imbalance ratio, this strategy effectively identifies the most informative instances, thereby reducing labeling costs. Additionally, a comprehensive imbalance-handling strategy is proposed, focusing on minority class samples not only during the creation of new classifiers but also by reusing minority instances at the sample level. A novel dynamic subensemble method is further developed to select the top-performing classifiers under current conditions, ensuring their inclusion in subsequent predictions and enhancing model performance. Extensive experiments on both real-world and synthetic data streams demonstrate that DEAL-DI significantly improves recall and G-mean compared to other semi-supervised methods, while simultaneously reducing labeling costs.

Keywords:

Ensemble learning

class imbalance

online active learning

concept drift

Dynamic Ensemble Active Learning for Drifting Imbalanced Data Streams

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