Enhancing Robustness in Feature Importance Methods with NAFIC and CESHAP for Improved Interpretability - Grigorios Tzionis; Georgia Kougka; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris; Maro Vlachopoulou (Applied Artificial Intelligence)
Understanding and interpreting machine learning models is crucial in high-stakes industries like steel manufacturing, where decisions impact energy efficiency and environmental sustainability. Traditional feature importance methods often struggle with robustness under noisy conditions, leading to unreliable insights. To address this problem, we introduce the Complexity and Interaction Enhanced SHAP (CESHAP), a novel feature importance method that incorporates model complexity and feature interactions. Alongside, we propose the Noise-Adjusted Feature Importance Change (NAFIC) metric to assess the robustness of feature importance methods against varying levels of noise. Experiments conducted on an energy consumption dataset from the steel industry, with systematically introduced Gaussian noise levels (5%, 10%, 15%, 20%), demonstrate that CESHAP offers superior robustness in tree-based models compared to SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI). Our findings underscore the effectiveness of CESHAP in enhancing interpretability and reliability in complex, non-linear models, ultimately supporting more informed decision-making in energy-intensive industries.