A streaming ensemble algorithm (SEA) for large-scale classification

Keywords

Computer science, Boosting (machine learning), Resampling, Machine learning, Decision tree

References

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• Learning with ensembles: How overfitting can be usefulPeter Sollich, Anders Krogh — 1995
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• Distributed Learning on Very Large Data SetsL. Hall, K. Bowyer, W. Kegelmeyer — 2000
• Experiments with a new boosting algorithmYoav Freund, Robert E. Schapire — 1996
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• Massive Support Vector RegressionO. Mangasarian, D. Musicant — 1999
• Bagging PredictorsLeo Breiman — 1996
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Cited by

• Real-time ranking of electrical feeders using expert adviceHila Becker, Marta Arias — 2007
• Positive Unlabeled Learning for Data Stream ClassificationXiaoli Li, Philip S. Yu, Bing Liu — 2009
• A comparative study of simple online learning strategies for streaming dataMónica Millán-Giraldo, J. Salvador Sánchez — 2008
• Seeking Variety: A Dynamic Model of Employee Blog Reading BehaviorParam Vir Singh, Nachiketa Sahoo, Tridas Mukhopadhyay — 2010
• Considering Currency in Decision Trees in the Context of Big DataDiana Hristova — 2014
• The Impact of Diversity on Online Ensemble Learning in the Presence of Concept DriftLeandro L. Minku — 2010
• Mining data streams using option trees (revised edition, 2004)Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer — 2004
• Augmenting Wargame AI with Data Mining TechnologyJohn Rushing, John Tiller, Steve Tanner — 2004
• A Hidden Markov Model for Collaborative FilteringNachiketa Sahoo, Param Vir Singh, Tridas Mukhopadhyay — 2012
• Privacy-preserving Classification of Data StreamsChing-Ming Chao — 2008
• Machine-learning classification techniques for the analysis and prediction of high-frequency stock directionMichael Rechenthin — 2014
• Hill-climbing feature selection for multi-stream ASRDavid Gelbart, Nelson Morgan, Alexey Tsymbal — 2009
• Using Case-based Reasoning for Spam FilteringSarah Jane Delany — 2006
• Decision Tree for Dynamic and Uncertain Data StreamsChunquan Liang, Shuicheng Yan, Qunliang Song — 2010
• The problem of concept drift: definitions and related workAlexey Tsymbal — 2004
• Improving Hoeffding TreesRichard Kirkby — 2007
• Non convex optimization techniques based on DC programming and DCA and evolution methods for clustering. (Techniques d'optimisation non convexe basée sur la programmation DC et DCA et méthodes évolutives pour la classification non supervisée)Minh-Tri Ta — 2014
• Online learning strategies for classification of static data streamsMónica Millán-Giraldo, J. Salvador Sánchez — 2008
• Learning from concept drifting data streams with unlabeled dataXindong Wu, Peipei Li, Xuegang Hu — 2010
• Detecting concept change in dynamic data streamsRussel Pears, Sripirakas Sakthithasan, Yun Sing Koh — 2014

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