---
格式版本: 2
标题: "BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis"
原文链接: "https://arxiv.org/abs/2608.10587"
发布日期: "2026-08-11"
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发现时间: "2026-08-12T11:26:18+08:00"
入库时间: "2026-08-12T03:31:01.022Z"
来源平台: "arXiv 学术论文搜索"
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AI打分: 5
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采集批次: "2026年8月12日2点04分20秒"
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去重键: "https://arxiv.org/abs/2608.10587"
---

## Computer Science > Machine Learning

## Title:BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis

Authors:[Jiaqi Qiu](https://arxiv.org/search/cs?searchtype=author&query=Qiu,+J), [Rob Goedhart](https://arxiv.org/search/cs?searchtype=author&query=Goedhart,+R), [Jannis Kurtz](https://arxiv.org/search/cs?searchtype=author&query=Kurtz,+J), [Inez M. Zwetsloot](https://arxiv.org/search/cs?searchtype=author&query=Zwetsloot,+I+M)

[View PDF](https://arxiv.org/pdf/2608.10587) [HTML (experimental)](https://arxiv.org/html/2608.10587v1)

> Abstract: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.

| Subjects: | Machine Learning (cs.LG) |
| --- | --- |
| Cite as: | [arXiv:2608.10587](https://arxiv.org/abs/2608.10587) \[cs.LG\] |
|  | (or [arXiv:2608.10587v1](https://arxiv.org/abs/2608.10587v1) \[cs.LG\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10587](https://doi.org/10.48550/arXiv.2608.10587) |

## Submission history

From: Jiaqi Qiu \[[view email](https://arxiv.org/show-email/c4cddd04/2608.10587)\]  
**\[v1\]** Tue, 11 Aug 2026 07:16:06 UTC (10,744 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10587) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
