Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Boosting× | Bagging Online× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1990–1997 | 2001 |
| Autor original≠ | Schapire, R. E.; Freund, Y. | Oza, N. C. & Russell, S. |
| Tipo≠ | Sequential ensemble (iterative reweighting) | Online ensemble (streaming bagging) |
| Fonte seminal≠ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | 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 | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. | 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. |
| ScholarGateConjunto de dados ↗ |
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