Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Robustne isolatsioonimets× | Isolation Forest× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2008–2019 | 2008 |
| Looja≠ | Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tüüp≠ | Robust ensemble anomaly detection | Unsupervised ensemble (random partitioning trees) |
| Algallikas≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 413–422. IEEE. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Rööpnimetused≠ | Robust iForest, noise-robust isolation forest, contamination-robust isolation forest, robust anomaly isolation | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | Robust Isolation Forest extends the classic Isolation Forest anomaly detector with strategies that reduce sensitivity to data contamination, masking effects, and biased random splits. By incorporating robustness mechanisms — such as improved subsampling, re-weighting of suspicious regions, or bias-corrected splitting — it achieves more reliable anomaly scores when the training data itself contains a non-trivial fraction of anomalies or when specific feature distributions cause standard iForest to produce unreliable path lengths. | 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. |
| ScholarGateAndmestik ↗ |
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