Classifier concept drift detection and the illusion of progress

Abstract

When a new concept drift detection method is proposed, a common way to show the benefits of the new method, is to use a classifier to perform an evaluation where each time the new algorithm detects change, the current classifier is replaced by a new one. Accuracy in this setting is considered a good measure of the quality of the change detector. In this paper we claim that this is not a good evaluation methodology and we show how a non-change detector can improve the accuracy of the classifier in this setting. We claim that this is due to the existence of a temporal dependence on the data and we propose not to evaluate concept drift detectors using only classifiers.