Linganisha mbinu

	Utambuzi wa Anomali kwa Kutumia Ensemble Autoencoder	Kikundi cha Kura (Voting Ensemble)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2017	1990s–2004
Mwanzilishi	Chen, J., Sathe, S., Aggarwal, C., & Turaga, D.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Aina	Ensemble unsupervised anomaly detection	Ensemble (combination of multiple classifiers by vote)
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.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Majina mbadala	ensemble AE anomaly detection, autoencoder ensemble outlier detection, multi-autoencoder anomaly scoring, AE ensemble unsupervised anomaly detection	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.