Statistical Tests to Identify Virtual Concept Drifts
Paulo M. Goncalves, Sylvain Chartier, Roberto Souto Maior de Barros · 2021
In streaming environments, concept drift is a common problem and identifying whether it is occurring is of utmost importance. Most of the published drift detection methods work based on the results of a base classifier, for example, by using the classification error or the distance between two consecutive errors. But if a change occurs only in the attributes space without changing the boundaries inferred by the learner, drift detection methods may not able to correctly work. This paper proposes VDDM, a drift detection method specially able to identify virtual concept drifts. It works by using a multivariate nonparametric statistical test to identify changes in a window of the most recent instances. Experimental results indicate that the usage of a multivariate nonparametric statistical test presents competitive results specially in the number of detected changes, distances to the drift point, sensitivity and specificity scores, as well as the Matthews Correlation Coefficient and the F1 score.