New ensemble methods for evolving data streams Export

@inproceedings{citeulike:5735447, abstract = {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.}, address = {New York, NY, USA}, author = {Bifet, Albert and Holmes, Geoff and Pfahringer, Bernhard and Kirkby, Richard and Gavald\`{a}, Ricard}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {5735447}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1557019.1557041}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1557019.1557041}, doi = {10.1145/1557019.1557041}, isbn = {978-1-60558-495-9}, keywords = {data-mining, kdd}, location = {Paris, France}, pages = {139--148}, posted-at = {2009-09-07 18:52:36}, priority = {2}, publisher = {ACM}, title = {New ensemble methods for evolving data streams}, url = {http://dx.doi.org/10.1145/1557019.1557041}, year = {2009} }

TY - CONF ID - citeulike:5735447 L3 - citeulike-article-id:5735447 N2 - 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. CY - Paris, France SN - 978-1-60558-495-9 AD - New York, NY, USA EP - 148 TI - New ensemble methods for evolving data streams PB - ACM T2 - KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining SP - 139 KW - data-mining KW - kdd AU - Bifet, Albert AU - Holmes, Geoff AU - Pfahringer, Bernhard AU - Kirkby, Richard AU - Gavaldà, Ricard PY - 2009/// UR - http://dx.doi.org/10.1145/1557019.1557041 ER -