Compară metode

	Detecția anomaliilor prin autoencoder cu învățare activă	Isolation Forest cu Învățare Activă
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2014–2018	2008–2019
Autorul original	Multiple (Guo et al.; Pimentel et al.)	Das, S. et al. (active anomaly discovery framework); Liu, F. T. et al. (Isolation Forest base)
Tip	Active learning + unsupervised deep anomaly detection hybrid	Active learning wrapper over isolation forest anomaly detector
Sursa seminală	Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249.	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.
Denumiri alternative	AL-Autoencoder anomaly detection, active autoencoder anomaly detection, query-guided autoencoder anomaly detection, active deep anomaly detection	AL-iForest, active anomaly detection with isolation forest, active isolation forest, query-guided isolation forest
Înrudite	6	5
Rezumat	Active Learning Autoencoder Anomaly Detection combines an autoencoder's unsupervised reconstruction-error scoring with an active learning query loop. The model flags high-error instances as candidate anomalies, selectively asks a human oracle to label the most informative ones, and iteratively retrains — achieving strong anomaly detection with only a small labeling budget.	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.