融合知识与数据的可解释学习，提升异常检测与诊断的透明度。
Knowledge-Augmented Explainable and Interpretable Learning for Anomaly Detection and Diagnosis
- 结合领域知识与数据驱动方法，增强模型可解释性。
- 覆盖从简单可解释模型到高级神经符号系统的多种技术。
- 适合医疗、工业等对可解释性要求高的高风险场景。
知识增强学习实现了基于知识与数据驱动方法的结合。在异常检测与诊断中，可理解性通常是一个关键因素，尤其是在高风险领域。因此，可解释性与可解释性也是此类场景中的重要标准。本章聚焦于知识增强的可解释与可解释学习，以提升可理解性、透明度以及最终的计算意义建构。我们通过实例展示了在异常检测与诊断领域中的不同方法与技术——从相对简单的可解释方法到更先进的神经符号方法。
原文摘要 · Abstract (English)
Knowledge-augmented learning enables the combination of knowledge-based and data-driven approaches. For anomaly detection and diagnosis, understandability is typically an important factor, especially in high-risk areas. Therefore, explainability and interpretability are also major criteria in such contexts. This chapter focuses on knowledge-augmented explainable and interpretable learning to enhance understandability, transparency and ultimately computational sensemaking. We exemplify different approaches and methods in the domains of anomaly detection and diagnosis - from comparatively simple interpretable methods towards more advanced neuro-symbolic approaches.
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