Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD

Documenti analoghi

• AcME-AD: Accelerated Model Explanations for Anomaly Detection
  di: Zaccaria, Valentina, et al.
  Pubblicazione: (2024)
• Enhancing Interpretability and Generalizability in Extended Isolation Forests
  di: Arcudi, Alessio, et al.
  Pubblicazione: (2023)
• Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI
  di: Frizzo, Davide, et al.
  Pubblicazione: (2024)
• Fault Identification Enhancement with Reinforcement Learning (FIERL)
  di: Zaccaria, Valentina, et al.
  Pubblicazione: (2024)
• Function Based Isolation Forest (FuBIF): A Unifying Framework for Interpretable Isolation-Based Anomaly Detection
  di: Arcudi, Alessio, et al.
  Pubblicazione: (2025)