DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts
Authors: name: Miguel Aspis, name: Sebastián A. Cajas Ordónez, name: Andrés L. Suárez-Cetrulo, name: Ricardo Simón Carbajo

Learning from non-stationary data streams subject to concept drift requires
models that can adapt on-the-fly while remaining resource-efficient. Existing
adaptive ensemble methods often rely on coarse-grained adaptation mechanisms or
simple voting schemes that fail to optimally leverage specialized knowledge.
This paper introduces DriftMoE, an online Mixture-of-Experts (MoE) architecture
that addresses these limitations through a novel co-training framework.
DriftMoE features a compact neural router that is co-trained alongside a pool
of incremental Hoeffding tree experts. The key innovation lies in a symbiotic
learning loop that enables expert specialization: the router selects the most
suitable expert for prediction, the relevant experts update incrementally with
the true label, and the router refines its parameters using a multi-hot
correctness mask that reinforces every accurate expert. This feedback loop
provides the router with a clear training signal while accelerating expert
specialization. We evaluate DriftMoE's performance across nine state-of-the-art
data stream learning benchmarks spanning abrupt, gradual, and real-world drifts
testing two distinct configurations: one where experts specialize on data
regimes (multi-class variant), and another where they focus on single-class
specialization (task-based variant). Our results demonstrate that DriftMoE
achieves competitive results with state-of-the-art stream learning adaptive
ensembles, offering a principled and efficient approach to concept drift
adaptation. All code, data pipelines, and reproducibility scripts are available
in our public GitHub repository: https://github.com/miguel-ceadar/drift-moe.