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	Online Voting Ensemble	Ensemble głosujący
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2001–2009	1990s–2004
Twórca	Oza, N. C. & Russell, S.; extended by Bifet et al.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Typ	Online ensemble (incremental majority vote)	Ensemble (combination of multiple classifiers by vote)
Źródło pierwotne	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.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Inne nazwy	streaming voting ensemble, incremental voting ensemble, online majority-vote ensemble, data-stream voting classifier	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Pokrewne	6	5
Podsumowanie	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.