方法对比
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| 在线随机森林× | 在线决策树× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2009 | 2000 |
| 提出者≠ | Saffari, A. et al. | Domingos, P. & Hulten, G. |
| 类型≠ | Incremental ensemble (streaming decision trees) | Incremental supervised classifier |
| 开创性文献≠ | 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 ↗ | Domingos, P., & Hulten, G. (2000). Mining very fast data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 71–80). ACM. link ↗ |
| 别名 | ORF, streaming random forest, incremental random forest, adaptive random forest | Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree |
| 相关 | 6 | 6 |
| 摘要≠ | 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. | An Online Decision Tree is a decision tree that grows incrementally from a continuous stream of data without revisiting past examples. The dominant algorithm, the Hoeffding Tree (VFDT), uses the Hoeffding bound to decide when enough examples have been seen at a node to split it confidently, enabling scalable, real-time classification on potentially infinite data streams. |
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