Linganisha mbinu

	Uchunguzi wa Hitilafu kwa Kutumia Kujifunza Amilifu na Kirejeshi Kiotomatiki	Utambuzi wa Anomali kwa Kutumia Ensemble Autoencoder
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2014–2018	2017
Mwanzilishi	Multiple (Guo et al.; Pimentel et al.)	Chen, J., Sathe, S., Aggarwal, C., & Turaga, D.
Aina	Active learning + unsupervised deep anomaly detection hybrid	Ensemble unsupervised anomaly detection
Chanzo asilia	Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249.	Chen, J., Sathe, S., Aggarwal, C., & Turaga, D. (2017). Outlier Detection with Autoencoder Ensembles. In Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 90–98. SIAM.
Majina mbadala	AL-Autoencoder anomaly detection, active autoencoder anomaly detection, query-guided autoencoder anomaly detection, active deep anomaly detection	ensemble AE anomaly detection, autoencoder ensemble outlier detection, multi-autoencoder anomaly scoring, AE ensemble unsupervised anomaly detection
Zinazohusiana	6	5
Muhtasari	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.	Ensemble Autoencoder Anomaly Detection trains multiple autoencoder neural networks on normal-class data and aggregates their reconstruction errors to produce a robust anomaly score. By combining diverse autoencoders rather than relying on one, the method stabilises outlier rankings and reduces sensitivity to random initialisation or suboptimal architecture choices.