Sammenlign metoder

	Online Voting Ensemble	Online Bagging
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2001–2009	2001
Ophavsperson	Oza, N. C. & Russell, S.; extended by Bifet et al.	Oza, N. C. & Russell, S.
Type	Online ensemble (incremental majority vote)	Online ensemble (streaming bagging)
Oprindelig kilde	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. 229–236.	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.
Aliasser	streaming voting ensemble, incremental voting ensemble, online majority-vote ensemble, data-stream voting classifier	incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag
Relaterede	6	4
Resumé	Online Voting Ensemble is an incremental ensemble method that maintains a pool of base classifiers — each updated continuously on arriving data — and combines their predictions through a weighted or unweighted majority vote. Designed for data streams, it adapts to non-stationary distributions without retraining from scratch, making it well-suited to real-time classification tasks where data arrives sequentially and concept drift may occur.	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.