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 |
| Published: |
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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