Enhancing Robustness in Feature Importance Methods with NAFIC and CESHAP for Improved Interpretability
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 un...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2515062 |
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| author | Grigorios Tzionis Georgia Kougka Ilias Gialampoukidis Stefanos Vrochidis Ioannis Kompatsiaris Maro Vlachopoulou |
| author_facet | Grigorios Tzionis Georgia Kougka Ilias Gialampoukidis Stefanos Vrochidis Ioannis Kompatsiaris Maro Vlachopoulou |
| author_sort | Grigorios Tzionis |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bef0097dc54f4ddeae08ca14000f5915 |
| institution | DOAJ |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-bef0097dc54f4ddeae08ca14000f59152025-08-20T03:09:49ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452025-12-0139110.1080/08839514.2025.2515062Enhancing Robustness in Feature Importance Methods with NAFIC and CESHAP for Improved InterpretabilityGrigorios Tzionis0Georgia Kougka1Ilias Gialampoukidis2Stefanos Vrochidis3Ioannis Kompatsiaris4Maro Vlachopoulou5Information Technologies Institute (ITI), Centre For Research and Technology Hellas (CERTH), Thessaloniki, GreeceInformation Technologies Institute (ITI), Centre For Research and Technology Hellas (CERTH), Thessaloniki, GreeceInformation Technologies Institute (ITI), Centre For Research and Technology Hellas (CERTH), Thessaloniki, GreeceInformation Technologies Institute (ITI), Centre For Research and Technology Hellas (CERTH), Thessaloniki, GreeceInformation Technologies Institute (ITI), Centre For Research and Technology Hellas (CERTH), Thessaloniki, GreeceDepartment of Applied Informatics, University of Macedonia, Thessaloniki, GreeceUnderstanding 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.https://www.tandfonline.com/doi/10.1080/08839514.2025.2515062 |
| spellingShingle | Grigorios Tzionis Georgia Kougka Ilias Gialampoukidis Stefanos Vrochidis Ioannis Kompatsiaris Maro Vlachopoulou Enhancing Robustness in Feature Importance Methods with NAFIC and CESHAP for Improved Interpretability Applied Artificial Intelligence |
| title | Enhancing Robustness in Feature Importance Methods with NAFIC and CESHAP for Improved Interpretability |
| title_full | Enhancing Robustness in Feature Importance Methods with NAFIC and CESHAP for Improved Interpretability |
| title_fullStr | Enhancing Robustness in Feature Importance Methods with NAFIC and CESHAP for Improved Interpretability |
| title_full_unstemmed | Enhancing Robustness in Feature Importance Methods with NAFIC and CESHAP for Improved Interpretability |
| title_short | Enhancing Robustness in Feature Importance Methods with NAFIC and CESHAP for Improved Interpretability |
| title_sort | enhancing robustness in feature importance methods with nafic and ceshap for improved interpretability |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2025.2515062 |
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