השוואת שיטות

	יער בידוד מונחה-עצמית	יער בידוד
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2008–2020s	2008
הוגה השיטה	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
סוג	Ensemble anomaly detector with self-supervised pre-training	Unsupervised ensemble (random partitioning trees)
מקור מכונן	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.
כינויים	SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
קשורות	4	5
תקציר	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.