DMAE: dual-memory active ensemble learning for multi-class imbalanced concept-drifting data streams
TL;DR
This work proposes DMAE, a dual-memory active ensemble learning method for multiclass imbalanced concept-drifting data streams, and proposes a composite sample-weighting formulation, PCN-Weight, which jointly models boundary difficulty, class-imbalance status, sample–prototype relations, and temporal decay to guide incremental training and strengthen minority-class and hard-region representations.
Abstract
Multiclass imbalance and concept drift often coexist in real-world data streams, and the challenge becomes more severe under limited labeling budgets, where existing online ensemble and active learning methods still struggle to preserve minority-class recognition while adapting to evolving concepts. To address this issue, we propose DMAE, a dual-memory active ensemble learning method for multiclass imbalanced concept-drifting data streams. DMAE integrates an ensemble classifier, a drift detector, an instance sliding window, a label sliding window, a long-term prototype memory, and an initialization training sequence into a unified online framework for prediction, querying, memory maintenance, and model updating. To improve label efficiency, we develop a variable-threshold uncertainty strategy based on a decomposable asymmetric margin-threshold matrix, which combines a global threshold with class-pair-level corrections to focus queries on genuinely ambiguous, minority-relevant regions while controlling annotation cost. We further design drift-strength-aware dual-memory initialization (SDMI) and a probationary soft-replacement strategy (PDSR), which adaptively balance recent information and long-term prototypes according to online-estimated drift strength, supporting both historical-pattern retention and rapid adaptation under abrupt, gradual, and mixed drifts. We also propose a composite sample-weighting formulation, PCN-Weight, which jointly models boundary difficulty, class-imbalance status, sample–prototype relations, and temporal decay to guide incremental training and strengthen minority-class and hard-region representations. Experiments on 15 synthetic data streams and 5 real-world imbalanced data streams show that DMAE achieves more stable overall performance than ten state-of-the-art ensemble baselines in Accuracy, Kappa, G-Mean, and Recall, while remaining robust under different labeling rates and key hyperparameter settings.