用可读方程识别异常，让检测逻辑透明可懂。
Unsupervised Symbolic Anomaly Detection
- 通过符号回归学习描述正常数据的可读方程
- 方程偏离程度即为异常分数，性能媲美顶尖方法
- 适合需要解释性的医疗、科研场景
我们提出SYRAN，一种基于符号回归的无监督异常检测方法。与传统在高维隐空间编码正常模式不同，该方法学习一组人类可读的方程，描述符号不变量：在正常数据上近似恒定的函数。数据偏离这些不变量即产生异常分数，使检测逻辑本身具备可解释性，而非依赖事后解释。实验表明，SYRAN具有高度可解释性，所生成方程对应已知科学或医学关系，同时保持与当前最优方法相当的异常检测性能。
原文摘要 · Abstract (English)
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.
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