A Lightweight Concept Drift Detection Ensemble

Abstract

Uncovering information from large data streams containing changes in the data distribution (concept drift) make online learning a challenge that is progressively becoming more relevant. This paper proposes Drift Detection Ensemble (DDE), a small ensemble classifier that aggregates the warnings and drift detections of three concept drift detectors aiming to improve the results of the individual methods using different strategies and configurations. DDE was programmed to use different default combinations of detectors depending on the chosen sensitivity of the ensemble. Experiments were performed against six drift detectors using their default configurations, comparing their results on multiple artificial datasets containing different frequencies and durations of concept drifts, as well as real-world datasets. Our results indicate that the best two methods were DDE versions and they were statistically superior to several detectors.