{"ID":2890946,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.18464","arxiv_id":"2507.18464","title":"DriftMoE: A Mixture of Experts Approach to Handle 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 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.","short_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 pape...","url_abs":"https://arxiv.org/abs/2507.18464","url_pdf":"https://arxiv.org/pdf/2507.18464v1","authors":"[\"Miguel Aspis\",\"Sebastián A. Cajas Ordónez\",\"Andrés L. Suárez-Cetrulo\",\"Ricardo Simón Carbajo\"]","published":"2025-07-24T14:39:20Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\"]","methods":"[\"Mixture of Experts\"]","has_code":false,"code_links":[{"ID":611826,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2890946,"paper_url":"https://arxiv.org/abs/2507.18464","paper_title":"DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts","repo_url":"https://github.com/miguel-ceadar/drift-moe","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}