2011 EnablingFastPredictionforEnsemb: Difference between revisions

From GM-RKB
Jump to navigation Jump to search
m (Text replacement - " Jing Gao" to " Jing Gao")
m (Text replacement - " ↵↵" to " ")
 
Line 1: Line 1:
* ([[2011_EnablingFastPredictionforEnsemb|Zhang et al., 2011]]) ⇒ [[author::Peng Zhang]], [[author::Jun Li]], [[author::Peng Wang]], [[author::Byron J. Gao]], [[author::Xingquan Zhu]], and [[author::Li Guo]]. ([[year::2011]]). “Enabling Fast Prediction for Ensemble Models on Data Streams.” In: [[proceedings::Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[conference::KDD-2011]]) Journal. ISBN:978-1-4503-0813-7 [http://dx.doi.org/10.1145/2020408.2020442 doi:10.1145/2020408.2020442]  
* ([[2011_EnablingFastPredictionforEnsemb|Zhang et al., 2011]]) ⇒ [[author::Peng Zhang]], [[author::Jun Li]], [[author::Peng Wang]], [[author::Byron J. Gao]], [[author::Xingquan Zhu]], and [[author::Li Guo]]. ([[year::2011]]). “Enabling Fast Prediction for Ensemble Models on Data Streams.” In: [[proceedings::Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[conference::KDD-2011]]) Journal. ISBN:978-1-4503-0813-7 [http://dx.doi.org/10.1145/2020408.2020442 doi:10.1145/2020408.2020442]


<B>Subject Headings:</B>  
<B>Subject Headings:</B>


== Notes ==
== Notes ==

Latest revision as of 21:40, 2 December 2023

Subject Headings:

Notes

Cited By

Quotes

Author Keywords

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

;

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2011 EnablingFastPredictionforEnsembXingquan Zhu
Peng Zhang
Jun Li
Peng Wang
Byron J. Gao
Li Guo
Enabling Fast Prediction for Ensemble Models on Data Streams10.1145/2020408.20204422011