Comparar métodos

	Detección de Anomalías con Autoencoder y Aprendizaje Activo	Bosque de Aislamiento con Aprendizaje Activo
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2014–2018	2008–2019
Autor original	Multiple (Guo et al.; Pimentel et al.)	Das, S. et al. (active anomaly discovery framework); Liu, F. T. et al. (Isolation Forest base)
Tipo	Active learning + unsupervised deep anomaly detection hybrid	Active learning wrapper over isolation forest anomaly detector
Fuente seminal	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.
Alias	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
Relacionados	6	5
Resumen	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.