Comparar métodos

	Conjunto de Votação Online	Comitê de Votação
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2001–2009	1990s–2004
Autor original	Oza, N. C. & Russell, S.; extended by Bifet et al.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Tipo	Online ensemble (incremental majority vote)	Ensemble (combination of multiple classifiers by vote)
Fonte seminal	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
Outros nomes	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
Relacionados	6	5
Resumo	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.