Σύγκριση μεθόδων

	Αυτο-επιβλεπόμενο Isolation Forest	Isolation Forest
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2008–2020s	2008
Δημιουργός	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Τύπος	Ensemble anomaly detector with self-supervised pre-training	Unsupervised ensemble (random partitioning trees)
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Συναφείς	4	5
Σύνοψη	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.