방법 비교

	온라인 배깅	배깅 (Bootstrap Aggregating)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2001	1996
창시자	Oza, N. C. & Russell, S.	Breiman, L.
유형	Online ensemble (streaming bagging)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
원전	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.
별칭	incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
관련	4	5
요약	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.