AI RESEARCH
Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI
arXiv CS.LG
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ArXi:2405.01158v4 Announce Type: replace Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest (EIF) AD method.