Salīdzināt metodes

	Pašuzraudzības izolācijas mežs	Isolation Forest
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2008–2020s	2008
Autors	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Tips	Ensemble anomaly detector with self-supervised pre-training	Unsupervised ensemble (random partitioning trees)
Pirmavots	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.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Citi nosaukumi	SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Saistītās	4	5
Kopsavilkums	Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data.	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.