arXiv:2506.16314cs.LGastro-ph.IM2025-06
为异常检测设计可解释的特征签名，帮助理解为何某事件被标记为异常。
Signatures to help interpretability of anomalies
- 提出异常特征签名机制，定位导致异常判断的关键特征。
- 通过可视化特征贡献度，提升异常检测结果的可解释性。
- 适合需要解释模型决策的天文学、医疗等高风险领域应用。
机器学习在输出决策或评分时常被视为黑箱，异常检测也不例外。通常情况下，天文学家需自行分析数据以理解某个事件为何被标记为异常。本文提出异常特征签名的概念，旨在通过突出显示导致异常判断的特征，提升异常检测结果的可解释性。
原文摘要 · Abstract (English)
Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the astronomer is left to independently analyze the data in order to understand why a given event is tagged as an anomaly. We introduce here idea of anomaly signature, whose aim is to help the interpretability of anomalies by highlighting which features contributed to the decision.
异常检测可解释性特征分析
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