Unsupervised Symbolic Anomaly Detection
This addresses the need for interpretable anomaly detection in domains like science and medicine, offering a novel approach but with incremental improvements in interpretability.
The paper tackles the problem of interpretable anomaly detection by proposing SYRAN, an unsupervised method that learns human-readable equations to describe normal data patterns, resulting in strong performance comparable to state-of-the-art methods while providing interpretability.
We propose SYRAN, an unsupervised anomaly detection method based on symbolic regression. Instead of encoding normal patterns in an opaque, high-dimensional model, our method learns an ensemble of human-readable equations that describe symbolic invariants: functions that are approximately constant on normal data. Deviations from these invariants yield anomaly scores, so that the detection logic is interpretable by construction, rather than via post-hoc explanation. Experimental results demonstrate that SYRAN is highly interpretable, providing equations that correspond to known scientific or medical relationships, and maintains strong anomaly detection performance comparable to that of state-of-the-art methods.