Wilcoxon Rank Sum Test Drift Detector

Online learning regards extracting information from large quantities of data (streams) usually affected by changes in the distribution (concept drift). Drift detectors are software that estimate the positions of these changes to substitute the base learner and ultimately improve accuracy. Statistical Test of Equal Proportions (STEPD) is a simple, well-known, efficient detector which uses a hypothesis test between two proportions to signal the concept drifts. However, despite identifying the existing drifts close to their correct positions, STEPD tends to identify many false positives. This article examines the application of the Wilcoxon rank sum statistical test for concept drift detection, proposing WSTD. Experiments run in the MOA framework using four artificial dataset generators, with abrupt and gradual drift versions of three sizes, as well as seven real-world datasets, suggest WSTD improves the detections of STEPD and other methods as well as their accuracies in many scenarios. (C) 2017 Elsevier B.V. All rights reserved.