Accurate detecting concept drift in evolving data streams

Abstract

Abstract Predictive models operating on the evolving data streams are dynamic. The performance of a model will deteriorate eventually when it suffers the effect of concept drift. The learning algorithms require the proper adaptive strategies to cope with the concept drifting data streams. In this paper, we propose a new concept drift detection approach that analyzes the consistency of prequential error rate using Hoeffding’s inequality to detect the concept drifts in data streams. The experimental results show that our proposed method outperforms in comparison with other state-of-the-art detectors in terms of true drift detection, false alarm and delay of detection.