Ensemble diversity in evolving data streams

Abstract

While diversity of ensembles has been studied in the context of static data, it has not still received such research interest for evolving data streams. This paper aims at analyzing the impact of concept drift on diversity measures calculated for streaming ensembles. We consider six popular diversity measures and adapt their calculations to data stream requirements. A comprehensive series of experiments reveals the potential of each measure for visualizing ensemble performance over time. Measures highlighted as capable of depicting sudden and virtual drifts over time are used as basis for detecting changes with the Page-Hinkley test. Experimental results demonstrate that the κ interrater agreement, disagreement, and double fault measures, although designed to quantify diversity, provide a means of detecting changes competitive to that using classification accuracy.