  Concept drift describes unforeseeable changes in the underlying distribution
of streaming data over time. Concept drift research involves the development of
methodologies and techniques for drift detection, understanding and adaptation.
Data analysis has revealed that machine learning in a concept drift environment
will result in poor learning results if the drift is not addressed. To help
researchers identify which research topics are significant and how to apply
related techniques in data analysis tasks, it is necessary that a high quality,
instructive review of current research developments and trends in the concept
drift field is conducted. In addition, due to the rapid development of concept
drift in recent years, the methodologies of learning under concept drift have
become noticeably systematic, unveiling a framework which has not been
mentioned in literature. This paper reviews over 130 high quality publications
in concept drift related research areas, analyzes up-to-date developments in
methodologies and techniques, and establishes a framework of learning under
concept drift including three main components: concept drift detection, concept
drift understanding, and concept drift adaptation. This paper lists and
discusses 10 popular synthetic datasets and 14 publicly available benchmark
datasets used for evaluating the performance of learning algorithms aiming at
handling concept drift. Also, concept drift related research directions are
covered and discussed. By providing state-of-the-art knowledge, this survey
will directly support researchers in their understanding of research
developments in the field of learning under concept drift.
