LEAF: Navigating Concept Drift in Cellular Networks. (arXiv:2109.03011v4 [cs.NI] UPDATED)

Operational networks commonly rely on machine learning models for many tasks,
including detecting anomalies, inferring application performance, and
forecasting demand. Yet, unfortunately, model accuracy can degrade due to
concept drift, whereby the relationship between the features and the target
prediction changes due to reasons ranging from software upgrades to seasonality
to changes in user behavior. Mitigating concept drift is thus an essential part
of operationalizing machine learning models, and yet despite its importance,
concept drift has not been extensively explored …

arxiv cellular concept networks