Linganisha mbinu

	Utambuzi wa Anomali kwa Kutumia Ensemble Autoencoder	Ugunduzi wa Anomaly kwa Kutumia Autoencoder za Nusu-Msimamizi
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2017	2018–2020
Mwanzilishi	Chen, J., Sathe, S., Aggarwal, C., & Turaga, D.	Ruff, L. et al.; Zong, B. et al.
Aina	Ensemble unsupervised anomaly detection	Semi-supervised deep anomaly detection
Chanzo asilia	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.	Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020).
Majina mbadala	ensemble AE anomaly detection, autoencoder ensemble outlier detection, multi-autoencoder anomaly scoring, AE ensemble unsupervised anomaly detection	Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection
Zinazohusiana	5	5
Muhtasari	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.	Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist.