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Online Bagging/Evidence
Method evidence record

Online Bagging

Online Bagging is a streaming ensemble method introduced by Oza and Russell in 2001 that adapts the classical bootstrap aggregating (Bagging) framework to the online learning setting. Instead of resampling a fixed dataset, each incoming instance is fed to every base learner a Poisson(1)-distributed number of times, faithfully approximating bootstrap sampling as the stream evolves. The result is a robust, incrementally updated ensemble that can handle concept drift and continuous data arrival without storing the entire dataset.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Online Bagging (Incremental Bootstrap Aggregating)
Taxonomic method record · ml-model / machine-learning
  • Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 105–112. · URL
  • Bifet, A., Holmes, G., Kirkby, R., & Pfahringer, B. (2010). MOA: Massive Online Analysis. Journal of Machine Learning Research, 11, 1601–1604. · URL
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyBaggingmachine-suggested · Relational suggestion, not evidence.Same method familyGradient Boostingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketOnline Boostingmachine-suggested · Relational suggestion, not evidence.Same method familyRandom Forestmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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