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Adapting to drifting data streams is a significant challenge in onlinelearning. Concept drift must be detected for effective model adaptation toevolving data properties. Concept drift can impact the data distributionentirely or partially, which makes it difficult for drift detectors toaccurately identify the concept drift. Despite the numerous concept driftdetectors in the literature, standardized procedures and benchmarks forcomprehensive evaluation considering the locality of the drift are lacking. Wepresent a novel categorization of concept drift based on its locality andscale. A systematic approach leads to a set of 2,760 benchmark problems,reflecting various difficulty levels following our proposed categorization. Weconduct a comparative assessment of 9 state-of-the-art drift detectors acrossdiverse difficulties, highlighting their strengths and weaknesses for futureresearch. We examine how drift locality influences the classifier performanceand propose strategies for different drift categories to minimize the recoverytime. Lastly, we provide lessons learned and recommendations for future conceptdrift research. Our benchmark data streams and experiments are publiclyavailable at https://github.com/gabrieljaguiar/locality-concept-drift.

A comprehensive analysis of concept drift locality in data streams