Unsupervised Symbolic Anomaly Detection

TL;DR

SYRAN is an unsupervised anomaly detection method that learns human-readable equations representing invariants in data, enabling interpretable and effective detection of anomalies across scientific and medical domains.

Contribution

The paper introduces SYRAN, a novel symbolic regression-based approach for anomaly detection that produces interpretable equations instead of opaque models.

Findings

SYRAN achieves detection performance comparable to state-of-the-art methods.

SYRAN produces equations that align with known scientific or medical relationships.

The method offers inherently interpretable anomaly detection logic.

Abstract

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.