2009 NewEnsembleMethodsforEvolvingDa

• (Bifet et al., 2009) ⇒ Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavaldà. (2009). “New Ensemble Methods for Evolving Data Streams.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557041

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database applications: Database Applications — Data Mining.
• General Terms: Algorithms

Cited By

• http://scholar.google.com/scholar?q=%22New+ensemble+methods+for+evolving+data+streams%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557041&preflayout=flat#citedby

Quotes

Author Keywords

Data Streams, Ensemble Methods, Concept Drift, Decision Trees

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 NewEnsembleMethodsforEvolvingDa	Albert Bifet Geoff Holmes Bernhard Pfahringer Richard Kirkby Ricard Gavaldà			New Ensemble Methods for Evolving Data Streams		KDD-2009 Proceedings		10.1145/1557019.1557041		2009