Σύγκριση μεθόδων

	Online Bagging	Bagging (Bootstrap Aggregating)	Online Boosting
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	2001	1996	2001
Δημιουργός	Oza, N. C. & Russell, S.	Breiman, L.	Oza, N. C. & Russell, S.
Τύπος	Online ensemble (streaming bagging)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Online ensemble (incremental boosting)
Θεμελιώδης πηγή	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.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.
Εναλλακτικές ονομασίες	incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting
Συναφείς	4	5	6
Σύνοψη	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments.