A survey proposes a novel 3D taxonomy classifying drifts into time stream, data stream, and model stream categories to unify research on non-stationary autonomous learning.
UDDA-TC: Unsupervised Real-Time Drift Detection and Adaptation for Continual Traffic Classification in Mobile Edge Computing,
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Autonomous Drift Learning in Data Streams: A Unified Perspective
A survey proposes a novel 3D taxonomy classifying drifts into time stream, data stream, and model stream categories to unify research on non-stationary autonomous learning.