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This paper proposes a novel approach that integrates permutation feature importance (PFI) with conformal prediction to enhance explainable artificial intelligence (XAI) in predictive process monitoring. By evaluating the significance of features alongside predictive uncertainty, the method aims to improve the robustness and interpretability of machine learning models in high-stakes environments. The study explores three scenarios of feature permutation to assess their impact on model predictions and confide

Author

Harish Panghal

Language

EN