2011 EnablingFastPredictionforEnsemb

• (Zhang et al., 2011) ⇒ Peng Zhang, Jun Li, Peng Wang, Byron J. Gao, Xingquan Zhu, and Li Guo. (2011). “Enabling Fast Prediction for Ensemble Models on Data Streams.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020442

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Cited By

• http://scholar.google.com/scholar?q=%222011%22+Enabling+Fast+Prediction+for+Ensemble+Models+on+Data+Streams
• http://dl.acm.org/citation.cfm?id=2020408.2020442&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; concept drifting; data mining; ensemble learning; performance; spatial indexing; stream classification; stream data mining

Abstract

Ensemble learning has become a common tool for data stream classification, being able to handle large volumes of stream data and concept drifting. Previous studies focus on building accurate prediction models from stream data. However, a linear scan of a large number of base classifiers in the ensemble during prediction incurs significant costs in response time, preventing ensemble learning from being practical for many real world time-critical data stream applications, such as Web traffic stream monitoring, spam detection, and intrusion detection. In these applications, data streams usually arrive at a speed of GB/second, and it is necessary to classify each stream record in a timely manner. To address this problem, we propose a novel Ensemble-tree (E-tree for short) indexing structure to organize all base classifiers in an ensemble for fast prediction. On one hand, E-trees treat ensembles as spatial databases and employ an R-tree like height-balanced structure to reduce the expected prediction time from linear to sub-linear complexity. On the other hand, E-trees can automatically update themselves by continuously integrating new classifiers and discarding outdated ones, well adapting to new trends and patterns underneath data streams. Experiments on both synthetic and real-world data streams demonstrate the performance of our approach.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 EnablingFastPredictionforEnsemb	Xingquan Zhu Peng Zhang Jun Li Peng Wang Byron J. Gao Li Guo			Enabling Fast Prediction for Ensemble Models on Data Streams				10.1145/2020408.2020442		2011