Abstract
Anomaly detection (AD) is a crucial process often required in industrial settings. Anomalies can signal underlying issues within a system, prompting further investigation. Industrial processes aim to streamline operations as much as possible, encompassing the production of the final product, making AD an essential mean to reach this goal.Conventional anomaly detection methodologies typically classify observations as either normal or anomalous without providing insight into the reasons behind these classifications.Consequently, in light of the emergence of Industry 5.0, a more desirable approach involves providing interpretable outcomes, enabling users to understand the rationale behind the results.This paper presents the first industrial application of ExIFFI, a recently developed approach focused on the production of fast and efficient explanations for the Extended Isolation Forest (EIF) Anomaly detection method. ExIFFI is tested on two publicly available industrial datasets demonstrating superior effectiveness in explanations and computational efficiency with the respect to other state-of-the-art explainable AD models.
Abstract (translated)
异常检测（AD）是工业环境中经常需要的关键过程。异常可以表明系统内部潜在的问题，从而促使进行进一步调查。工业过程旨在尽可能简化操作，包括生产最终产品的过程，因此AD成为实现这一目标的基本手段。传统的异常检测方法通常将观察结果分为正常或异常两类，而没有提供这些分类背后的原因。因此，考虑到工业4.0的兴起，一种更令人满意的方法是提供可解释的结果，使用户能够理解结果背后的推理过程。本文介绍了ExIFFI这一新方法在工业领域的首次应用，该方法专注于为Extended Isolation Forest（EIF）异常检测方法生产快速且高效的解释。ExIFFI在两个公开可用的工业数据集上的测试表明，与其他最先进的可解释AD模型相比，其解释性和计算效率具有优越性。
URL
https://arxiv.org/abs/2405.01158
