Methoden vergelijken

	Actief Lerend Isolatiebos	Autoencoder Anomaliedetectie
Vakgebied	Machine learning	Machine learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2008–2019	2006–2014
Grondlegger	Das, S. et al. (active anomaly discovery framework); Liu, F. T. et al. (Isolation Forest base)	Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s
Type	Active learning wrapper over isolation forest anomaly detector	Unsupervised deep learning (reconstruction-based)
Oorspronkelijke bron	Das, S., Wong, W. K., Fern, A., Dietterich, T. G., & Amran Siddiqui, M. (2019). Incorporating Expert Feedback into Active Anomaly Discovery. In Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), pp. 1009–1014.	Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
Aliassen	AL-iForest, active anomaly detection with isolation forest, active isolation forest, query-guided isolation forest	AE anomaly detection, reconstruction-error anomaly detection, deep autoencoder outlier detection, unsupervised autoencoder anomaly detection
Verwant	5	3
Samenvatting	Active Learning Isolation Forest combines the unsupervised anomaly-scoring power of Isolation Forest with an iterative query strategy that asks a human expert to label the most informative instances. The result is a detector that refines its anomaly boundaries using a minimal labeling budget, dramatically improving precision on rare and subtle anomalies compared to a purely unsupervised baseline.	Autoencoder anomaly detection trains a neural network to compress and then reconstruct normal data. Because the model has only ever learned what normal looks like, anomalous inputs produce noticeably higher reconstruction errors — and those errors become the anomaly score. The method requires no labeled anomalies and scales naturally to high-dimensional data such as sensor streams, images, and log records.