Ensemble of Precision-Recall Curve (PRC) Classification Trees with Autoencoders

Anomaly detection underpins critical applications--from network security and intrusion detection to fraud prevention--where recognizing aberrant patterns rapidly is indispensable. Progress in this area is routinely impeded by two obstacles: extreme class imbalance and the curse of dimensionality. To combat the former, we previously introduced Precision-Recall Curve (PRC) classification trees and their ensemble extension, the PRC Random Forest (PRC-RF). Building on that foundation, we now propose a hybrid framework that integrates PRC-RF with autoencoders--unsupervised machine learning methods that learn compact latent representations--to confront both challenges simultaneously. Extensive experiments across diverse benchmark datasets demonstrate that the resulting Autoencoder-PRC-RF model achieves superior accuracy, scalability, and in-terpretability relative to prior methods, affirming its potential for high-stakes anomaly-detection tasks.