Successive Halving Based Online Ensemble Selection for Concept-Drift Adaptation
Jobin Wilson, Santanu Chaudhury, Brejesh Lall · IEEE Transactions on Artificial Intelligence · 2025
Ensemble learning is one of the most successful approaches for concept-drift adaptation due to its versatility and high predictive performance. However, a practical challenge in using ensembles for high-speed data stream mining is the associated large computational cost. In this paper, we introduce a computationally efficient heterogeneous ensemble classifier named SUHEN (Successive Halving Ensemble) which adapts to concept-drift using online ensemble selection. We model ensemble selection as a fixed budget best arm identification bandit problem and solve it using Successive Halving Algorithm (SHA). SUHEN identifies a single best performing member for a stream segment and utilizes it for training and prediction until a drift is detected. Upon detecting drift, SHA identifies the new best performer for the segment. As stream characteristics evolve, manually choosing a fixed SHA budget would be challenging. To this end, we extend SUHEN by posing budget selection as a hyperparameter tuning problem and solve it using meta-learning. Our evaluation on 20 benchmark datasets reveal that SUHEN provides accuracy statistically at par with state-of-the-art ensemble algorithms, while providing significant computational resource savings. This makes our proposal attractive for high-speed stream mining problems in resource-constrained settings.