Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Random Forest Online× | Bagging Online× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2009 | 2001 |
| Autor original≠ | Saffari, A. et al. | Oza, N. C. & Russell, S. |
| Tipo≠ | Incremental ensemble (streaming decision trees) | Online ensemble (streaming bagging) |
| Fonte seminal≠ | Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE. link ↗ | 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. link ↗ |
| Outros nomes | ORF, streaming random forest, incremental random forest, adaptive random forest | incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time. | 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. |
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