Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Online Isolation Forest× | Isolation Forest× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2008–2011 | 2008 |
| Autorul original≠ | Tan, S. C.; Ting, K. M.; Liu, T. F. (streaming variant); original iForest by Liu et al. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tip≠ | Streaming anomaly detection (online ensemble) | Unsupervised ensemble (random partitioning trees) |
| Sursa seminală≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Denumiri alternative≠ | streaming isolation forest, incremental isolation forest, online iForest, adaptive isolation forest | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Înrudite≠ | 6 | 5 |
| Rezumat≠ | Online Isolation Forest extends the Isolation Forest anomaly-detection algorithm to streaming or continuously arriving data. Instead of rebuilding isolation trees from scratch when new observations arrive, the forest is updated incrementally so that anomaly scores remain current without reprocessing the entire history. This makes it practical for real-time monitoring, fraud detection, and sensor-data surveillance where data volumes grow indefinitely. | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. |
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