document: 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 applications demanding such processing increases . Online mining when such data streams evolve over time , that is when concepts 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 concept 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 million examples shows that the new ensemble methods perform very well compared to several known methods .
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