Learning in the presence of concept drift and hidden contexts

  title={Learning in the presence of concept drift and hidden contexts},
  author={Gerhard Widmer and Miroslav Kubat},
  journal={Machine Learning},
On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2… CONTINUE READING

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