Σύγκριση μεθόδων

	Ανίχνευση Ανωμαλιών με Αυτοκωδικοποιητή Αυτο-εποπτευόμενης Μάθησης	Isolation Forest
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2018–2020	2008
Δημιουργός	Golan & El-Yaniv; broader self-supervised anomaly detection community	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Τύπος	Unsupervised / self-supervised deep learning	Unsupervised ensemble (random partitioning trees)
Θεμελιώδης πηγή	Golan, I. & El-Yaniv, R. (2018). Deep one-class classification via geometric transformations. Advances in Neural Information Processing Systems (NeurIPS), 31.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Εναλλακτικές ονομασίες	SSL Autoencoder anomaly detection, self-supervised reconstruction anomaly detection, pretext-task autoencoder anomaly detection, contrastive autoencoder anomaly detection	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Συναφείς	6	5
Σύνοψη	Self-supervised autoencoder anomaly detection trains an autoencoder using self-supervised pretext tasks — such as predicting geometric transformations or solving jigsaw puzzles — on unlabeled normal data, then flags as anomalous any input whose reconstruction error or pretext-task score deviates substantially from the learned normal distribution.	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.