Abstract
The evolution of data or concept drift is a common phenomena in data streams. Currently most drift detection methods are able to locate the point of drift, but are unable to provide important information on the characteristics of change such as the magnitude of change which we refer to as drift severity. Monitoring drift severity provides crucial information to users allowing them to formulate a more adaptive response. In this paper, we propose a drift detector, MagSeed, which is capable of tracking drift severity with a high rate of true positives and a low rate of false positives. We evaluate MagSeed on synthetic and real world data, and compare it to state of the art drift detectors ADWIN2 and DDM.
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Chen, K., Koh, Y.S., Riddle, P. (2015). Tracking Drift Severity in Data Streams. In: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_9
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DOI: https://doi.org/10.1007/978-3-319-26350-2_9
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