Abstract
Conference Title: 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) Conference Start Date: 2018, Aug. 20 Conference End Date: 2018, Aug. 24 Conference Location: Munich, Germany Concept drifts can occur in data streams, which disable any models built on static data distribution to fit dynamic or recurrent concepts. If there is a feature that can monitor the stableness of such distribution, then we have a proper reference to adapt the model. Based on this idea, we propose a novel approach named Multiscale Drift Detection Test (MDDT) that localizes abrupt drift points when detection feature values fluctuate. MDDT is based on a resampling scheme and a paired student t-test. It applies a detection procedure on a broad and a narrow scale. This multiscale structure not only reduces massive time of a constant checking process, but also filters noise in the detection features. Experiments are performed via synthetic and real-world datasets. The results indicate that the proposed method outperforms the state-of-art algorithms in terms of computation cost and average accuracy.