Automatic Learning to Detect Concept Drift
  Many methods have been proposed to detect concept drift, i.e., the change in
the distribution of streaming data, due to concept drift causes a decrease in
the prediction accuracy of algorithms. However, the most of current detection
methods are based on the assessment of the degree of change in the data
distribution, cannot identify the type of concept drift. In this paper, we
propose Active Drift Detection with Meta learning (Meta-ADD), a novel framework
that learns to classify concept drift by tracking the changed pattern of error
rates. Specifically, in the training phase, we extract meta-features based on
the error rates of various concept drift, after which a meta-detector is
developed via a prototypical neural network by representing various concept
drift classes as corresponding prototypes. In the detection phase, the learned
meta-detector is fine-tuned to adapt to the corresponding data stream via
stream-based active learning. Hence, Meta-ADD uses machine learning to learn to
detect concept drifts and identify their types automatically, which can
directly support drift understand. The experiment results verify the
effectiveness of Meta-ADD.
