LLM Reasoning 相关度: 5/10

Unsupervised Symbolic Anomaly Detection

Md Maruf Hossain, Tim Katzke, Simon Klüttermann, Emmanuel Müller
arXiv: 2603.17575v1 发布: 2026-03-18 更新: 2026-03-18

AI 摘要

SYRAN是一种基于符号回归的无监督异常检测方法,可生成人类可读的方程进行异常检测。

主要贡献

  • 提出了一种基于符号回归的无监督异常检测方法SYRAN
  • 学习人类可读的方程来描述符号不变量
  • 检测逻辑具有可解释性

方法论

利用符号回归学习数据中的不变量方程,通过偏离这些不变量来识别异常,无需复杂的后验解释。

原文摘要

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.

标签

异常检测 符号回归 无监督学习 可解释性

arXiv 分类

cs.LG cs.AI cs.SC