Interpretable Data-driven Anomaly Detection in Industrial Processes with ExIFFI
This provides interpretable outcomes for users in Industry 5.0 to understand model decisions, but it is incremental as it applies a recent method to new data.
The paper tackled the problem of interpretable anomaly detection in industrial processes by applying ExIFFI to the Extended Isolation Forest method, achieving superior explanation effectiveness and computational efficiency on three industrial datasets.
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. ExIFFI is tested on three industrial datasets, demonstrating superior explanation effectiveness and computational efficiency compared to other state-of-the-art explainable AD models.