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[2602.12976] Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling
Summary
The paper presents VAE++ESDD, a novel approach for anomaly detection in streaming data using drift-aware variational autoencoders and two-level ensembling techniques.
Why It Matters
As data generation accelerates, identifying anomalies in unlabeled, nonstationary environments is crucial. This research addresses the challenges of concept drift, enhancing the reliability of anomaly detection methods in real-world applications.
Key Takeaways
- Introduces VAE++ESDD for effective anomaly detection.
- Utilizes two-level ensembling to enhance prediction accuracy.
- Demonstrates superior performance over existing methods in various datasets.
Computer Science > Machine Learning arXiv:2602.12976 (cs) [Submitted on 13 Feb 2026] Title:Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling Authors:Jin Li, Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou View a PDF of the paper titled Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling, by Jin Li and 3 other authors View PDF HTML (experimental) Abstract:In today's digital world, the generation of vast amounts of streaming data in various domains has become ubiquitous. However, many of these data are unlabeled, making it challenging to identify events, particularly anomalies. This task becomes even more formidable in nonstationary environments where model performance can deteriorate over time due to concept drift. To address these challenges, this paper presents a novel method, VAE++ESDD, which employs incremental learning and two-level ensembling: an ensemble of Variational AutoEncoder(VAEs) for anomaly prediction, along with an ensemble of concept drift detectors. Each drift detector utilizes a statistical-based concept drift mechanism. To evaluate the effectiveness of VAE++ESDD, we conduct a comprehensive experimental study using real-world and synthetic datasets characterized by severely or extremely low anomalous rates and various drift characteristics. Our study reveals that the proposed method significantly outperforms both strong baselines and state-of-the-art methods. Comments: ...