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.

翻译：我们提出SYRAN，一种基于符号回归的无监督异常检测方法。该方法并非将正常模式编码于不透明的高维模型中，而是学习一组可人工解读的方程集合以描述符号不变量：即在正常数据上近似恒定的函数。这些不变量的偏差可生成异常分数，使得检测逻辑具有先天可解释性，而非依赖事后解释。实验结果表明，SYRAN具备高度可解释性，能够提供符合已知科学或医学关系的方程，同时保持与最先进方法相当的强异常检测性能。