Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R. & Gavalda, R. (2009). New ensemble methods for evolving data streams. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, Paris, France, June28-July 01 2009. (pp. 139-148). New York, USA: ACM.
Permanent Research Commons link: https://hdl.handle.net/10289/3982
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.
This is an author’s accepted version of an article published in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. © 2009 ACM.