Methoden vergelijken

	Boosting	Online Bagging
Vakgebied	Machine learning	Machine learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	1990–1997	2001
Grondlegger	Schapire, R. E.; Freund, Y.	Oza, N. C. & Russell, S.
Type	Sequential ensemble (iterative reweighting)	Online ensemble (streaming bagging)
Oorspronkelijke bron	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	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.
Aliassen	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag
Verwant	6	4
Samenvatting	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.