2022/12/02 by Fabian Hinder, Valerie Vaquet, Hinder, Fabian +5
Computer Science · Decision Sciences · Environmental Science · #Advanced Bandit Algorithms Research #Air Quality Monitoring and Forecasting #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2212.01223
openalex publication_date 2022/12/02 · openalex created_date 2022/12/17 · openalex updated_date 2026/07/28
The notion of concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models may become inaccurate and need adjustment. Many technologies for learning with drift rely on the interleaved test-train error (ITTE) as a quantity which approximates the model generalization error and triggers drift detection and model updates. In this work, we investigate in how far this procedure is mathematically justified. More precisely, we relate a change of the ITTE to the presence of real drift, i.e., a changed posterior, and to a change of the training result under the assumption of optimality. We support our theoretical findings by empirical evidence for several learning algorithms, models, and datasets.