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Detecting Concept Drift With Neural Network Model Uncertainty. (arXiv:2107.01873v2 [cs.LG] UPDATED)
Sept. 26, 2022, 1:11 a.m. | Lucas Baier, Tim Schlör, Jakob Schöffer, Niklas Kühl
cs.LG updates on arXiv.org arxiv.org
Deployed machine learning models are confronted with the problem of changing
data over time, a phenomenon also called concept drift. While existing
approaches of concept drift detection already show convincing results, they
require true labels as a prerequisite for successful drift detection.
Especially in many real-world application scenarios-like the ones covered in
this work-true labels are scarce, and their acquisition is expensive.
Therefore, we introduce a new algorithm for drift detection, Uncertainty Drift
Detection (UDD), which is able to detect …
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