A Selective Detector Ensemble for Concept Drift Detection

Concept drifts usually originate from many causes instead of only one, which result in two types of concept drifts: abrupt drifts and gradual drifts. From the point of view of speed, concept drifts pose strong challenges for data stream mining. In this paper, we propose a selective detector ensemble to detect both abrupt and gradual drifts. We first present our detector ensemble construction method, and then introduce how to use this ensemble to detect concept drifts with the proposed early-findearly-report rule.To evaluate the performance of our method, we compare it with four drift detection methods on eight publicly available data sets containing various concept drifts. The experimental results show that compared with those benchmarks, our ensemble method can effectively improve the recall and false negative rate without significantly increasing the false positive rate, and has stronger generalization ability than those single-change-indicator-based methods.