Meta-ADD: A meta-learning based pre-trained model for concept drift active detection

by representing various concept drift classes as corresponding prototypes. In the detection phase, the meta-detector is fine-tuned to adapt to the real data stream via a simple stream-based active learning. Hence, Meta-ADD does not need a hypothesis test to detect concept drifts and identify their types automatically, which can directly support drift understand. The experiment results verify the effectiveness of Meta-ADD.