Methoden vergleichen

	Selbstüberwachte Autoencoder-Anomalieerkennung	Isolation Forest
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2018–2020	2008
Urheber	Golan & El-Yaniv; broader self-supervised anomaly detection community	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Typ	Unsupervised / self-supervised deep learning	Unsupervised ensemble (random partitioning trees)
Wegweisende Quelle	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.
Aliasnamen	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
Verwandt	6	5
Zusammenfassung	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.