DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts
Miguel Aspis, Sebasti\'an A. Cajas Ord\'onez, Andr\'es L. Su\'arez-Cetrulo, Ricardo Sim\'on Carbajo

TL;DR
DriftMoE introduces an online Mixture-of-Experts architecture with a co-training framework that enhances adaptation to concept drift in data streams through expert specialization and resource-efficient learning.
Contribution
It presents a novel co-training MoE framework with a neural router and incremental experts, improving concept drift handling over existing ensemble methods.
Findings
Competitive performance on nine data stream benchmarks
Effective expert specialization in different data regimes
Resource-efficient adaptation to various concept drifts
Abstract
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…
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Taxonomy
TopicsData Stream Mining Techniques · Advanced Bandit Algorithms Research
