Abstract
Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Many technologies for learning with drift rely on the interleaved test-train error (ITTE) as a quantity to evaluate model performance and trigger drift detection and model updates. Online learning theory mainly focuses on providing generalization bounds for future loss. Usually, these bounds are too loose to be of practical use. Improving them further is not easily possible as they are tight in many cases. In this work, a new theoretical framework focusing on more practical questions is presented: change of training result, optimal models, and ITTE in the presence (and type) of drift. We support our theoretical findings with empirical evidence for several learning algorithms, models, and datasets.
We gratefully acknowledge funding by the BMBF TiM, grant number 05M20PBA.
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Hinder, F., Vaquet, V., Brinkrolf, J., Hammer, B. (2023). On the Change of Decision Boundary and Loss in Learning with Concept Drift. In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_15
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