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	Samonadzorowany las izolacyjny	Isolation Forest
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2008–2020s	2008
Twórca	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Typ	Ensemble anomaly detector with self-supervised pre-training	Unsupervised ensemble (random partitioning trees)
Źródło pierwotne	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.
Inne nazwy	SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Pokrewne	4	5
Podsumowanie	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.