New ensemble methods for evolving data streams

Advanced analysis of data streams is quickly becoming a key area of data mining research as the number of applica-tions demanding such processing increases. Online mining when such data streams evolve over time, that is when con-cepts drift or change completely, is becoming one of the core issues. When tackling non-stationary concepts, ensembles of classifiers have several advantages over single classifier methods: they are easy to scale and parallelize, they can adapt to change quickly by pruning under-performing parts of the ensemble, and they therefore usually also generate more accurate concept descriptions. This paper proposes a new experimental data stream framework for studying con-cept drift, and two new variants of Bagging: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. Using the new experimental framework, an evaluation study on synthetic and real-world datasets comprising up to ten mil-lion examples shows that the new ensemble methods perform very well compared to several known methods