Unsupervised Ensemble

Unsupervised ensemble learning focuses on combining predictions from multiple models without relying on labeled training data, aiming to improve accuracy and robustness compared to individual models. Current research explores various ensemble architectures, including weighted voting, stacking, and novel approaches like oblique forest autoencoders and instance-wise methods that adapt model weights based on individual data points. This field is significant for applications where labeled data is scarce or expensive, such as anomaly detection in industrial control systems, risk gene discovery, and seismic data processing, offering improved efficiency and accuracy in these domains.