发表机构
University of Amsterdam; Amsterdam Business School（阿姆斯特丹大学; 阿姆斯特丹商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有异常诊断方法的缺陷，提出可扩展的基线参考诊断方法BREAD，经数学保证和实验验证，其诊断结果比LIME更忠实准确，适用于AI前瞻性异常检测场景。
AI 中文摘要
基于人工智能（AI）的前瞻性异常检测方法正越来越多地应用于高维非线性场景中。在这些方法中，基于AI的统计过程监控（SPM）被广泛使用，为前瞻性监控提供了结构化框架。一旦检测到异常，就需要诊断方法来识别导致被标记观测偏离正常行为的特征。传统SPM诊断方法通常针对特定检测模型设计，无法直接应用于基于AI的方法。模型不可知的可解释AI（XAI）为特征相关性解释提供了通用框架，但现有方法存在可扩展性限制，或为噪声特征分配相关性，降低了诊断准确性。我们提出了一种可扩展的、基线参考的诊断方法，该方法同时利用异常观测和正常基线信息。我们提供了数学保证：在均值偏移异常设置下，与LIME相比，所提方法在检测导致异常的特征方面实现了更高的忠实度。模拟研究和实际案例研究验证了所提方法的有效性，表明其能为基于AI的前瞻性异常检测方法生成更忠实、更准确的诊断结果。
英文摘要
Artificial Intelligence (AI)-based prospective anomaly detection methods are increasingly deployed in high-dimensional and nonlinear settings. Among these approaches, AI-based statistical process monitoring (SPM) is widely used, providing a structured framework for prospective monitoring. Once an anomaly is detected, a diagnosis method is needed to identify the features driving the flagged observation away from normal behaviour. Traditional SPM diagnosis methods are typically designed for specific detection models and cannot be directly applied to AI-based methods. Model-agnostic explainable AI (XAI) offers a general framework for feature relevance explanation. However, existing methods suffer from scalability limitations or assign relevance to noise features, reducing diagnosis accuracy. We propose a scalable, baseline-referenced diagnosis method that uses both the anomalous observation and normal baseline information. We provide mathematical guarantees that under a mean-shift anomaly setting, the proposed method achieves higher faithfulness in detecting the features causing the anomaly compared to LIME. Simulation studies and a real-world case study validate the effectiveness of the proposed method and show that it generates more faithful and accurate diagnosis results for AI-based prospective anomaly detection methods.
