Fair Ensemble Learning for Imbalanced Data Streams

Learning from data streams requires online updates and stable predictive performance. However, class imbalance, sensitive-group imbalance, and instance-level difficulty imbalance are widespread in real-world data streams, where they reshape online training signals and may induce or amplify unfair predictions. Their distinct fairness effects and the limitations of existing fairness-aware methods under multi-source imbalance remain insufficiently understood. We analyze 204 controlled stationary synthetic streams using a unified profile-based prequential protocol. The results reveal distinct effects of the three imbalance sources on online fairness and highlight the challenges of maintaining fairness under their combined influence. Existing fairness interventions also exhibit source-dependent limitations and can incur substantial accuracy costs. Guided by these findings, we propose instance reWeighting for fair Ensemble Learning under imbaLanced data streams (WELL). WELL jointly reweights the incoming instance according to label-sensitive cells and moderates within-cell difficulty, with theoretical conditions for fairness improvement and bounds on accuracy loss. Across five real-world streams and 19 synthetic settings, WELL outperforms state-of-the-art fairness-aware streaming methods in the overall accuracy-fairness trade-off.