Divide, Route, and Conquer: DriftMoE's Smart Take on Concept Drift
Concept drift is the curse of the real world. Models trained on yesterday’s data go stale in hours, sometimes minutes. Traditional remedies like Adaptive Random Forests (ARF) respond reactively, detecting change and resetting trees. But what if the system could instead continuously learn where to look, dynamically routing each new sample to the right expert — no drift detector required? That’s exactly the ambition behind DriftMoE, a Mixture-of-Experts framework purpose-built for online learning in non-stationary environments. Co-developed by researchers at Ireland’s CeADAR, this architecture marries lightweight neural routing with classic Hoeffding trees, achieving expert specialization as a byproduct of learning — not as a bolted-on correction. ...