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With today's abundant streams of data, the only constant we can rely on ischange. For stream classification algorithms, it is necessary to adapt toconcept drift. This can be achieved by monitoring the model error, andtriggering counter measures as changes occur. In this paper, we propose a driftdetection mechanism that fits a beta distribution to the model error, andtreats abnormal behavior as drift. It works with any given model, leveragesprior knowledge about this model, and allows to set application-specificconfidence thresholds. Experiments confirm that it performs well, in particularwhen drift occurs abruptly.

Beta Distribution Drift Detection for Adaptive Classifiers