Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach
Anomaly detection methods often have uncertain behavior with respect to samples near the distribution boundary, limiting their ability to anticipate future anomalies. This work introduces the concept of near-anomalies that, while not yet anomalous, lie close to the boundary and are likely to transition into anomalies in the near future. To address this, we propose an unsupervised method, named Christoffel-based ANomaly Anticipation for eaRly dIscovery (CANARI), which leverages the strong theoretical foundations of the Christoffel function to detect near-anomalies. The method is validated on industrial in-circuit testing data from printed circuit boards, with synthetically generated near-a...
| Authors | L{é}a Billet, Louise Trav{é}-Massuy{è}s, Elodie Chanthery et al. |
|---|---|
| Primary category | stat.ML |
| Published | 29 July 2026 |
| Last updated at source | 3 August 2026 |
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