Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
This study introduces a novel framework leveraging Rough Set Theory (RST)-based feature selection—MLReduct, MLSpecialReduct, and MLFuzzyRoughSet—to enhance machine learning performance on uncertain data. Applied to a private cardiovascular dataset, our MLSpecialReduct algorithm achieves a peak Rando...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/5148 |
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| author | Sami Naouali Oussama El Othmani |
| author_facet | Sami Naouali Oussama El Othmani |
| author_sort | Sami Naouali |
| collection | DOAJ |
| description | This study introduces a novel framework leveraging Rough Set Theory (RST)-based feature selection—MLReduct, MLSpecialReduct, and MLFuzzyRoughSet—to enhance machine learning performance on uncertain data. Applied to a private cardiovascular dataset, our MLSpecialReduct algorithm achieves a peak Random Forest accuracy of 0.99 (versus 0.85 without feature selection), while MLFuzzyRoughSet improves accuracy to 0.83, surpassing our MLVarianceThreshold (0.72–0.77), an adaptation of the traditional VarianceThreshold method. We integrate these RST techniques with preprocessing (discretization, normalization, encoding) and compare them against traditional approaches across classifiers like Random Forest and Naive Bayes. The results underscore RST’s edge in accuracy, efficiency, and interpretability, with MLSpecialReduct leading in minimal attribute reduction. Against baseline classifiers without feature selection and MLVarianceThreshold, our framework delivers significant improvements, establishing RST as a vital tool for explainable AI (XAI) in healthcare diagnostics and IoT systems. These findings open avenues for future hybrid RST-ML models, providing a robust, interpretable solution for complex data challenges. |
| format | Article |
| id | doaj-art-70f1a211249d47f5a5be704ebae261d3 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-70f1a211249d47f5a5be704ebae261d32025-08-20T02:59:11ZengMDPI AGApplied Sciences2076-34172025-05-01159514810.3390/app15095148Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML ModelsSami Naouali0Oussama El Othmani1Information Systems Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 31982, Saudi ArabiaInformation Systems Department, Military Academy of Fondouk Jedid, Nabeul 8012, TunisiaThis study introduces a novel framework leveraging Rough Set Theory (RST)-based feature selection—MLReduct, MLSpecialReduct, and MLFuzzyRoughSet—to enhance machine learning performance on uncertain data. Applied to a private cardiovascular dataset, our MLSpecialReduct algorithm achieves a peak Random Forest accuracy of 0.99 (versus 0.85 without feature selection), while MLFuzzyRoughSet improves accuracy to 0.83, surpassing our MLVarianceThreshold (0.72–0.77), an adaptation of the traditional VarianceThreshold method. We integrate these RST techniques with preprocessing (discretization, normalization, encoding) and compare them against traditional approaches across classifiers like Random Forest and Naive Bayes. The results underscore RST’s edge in accuracy, efficiency, and interpretability, with MLSpecialReduct leading in minimal attribute reduction. Against baseline classifiers without feature selection and MLVarianceThreshold, our framework delivers significant improvements, establishing RST as a vital tool for explainable AI (XAI) in healthcare diagnostics and IoT systems. These findings open avenues for future hybrid RST-ML models, providing a robust, interpretable solution for complex data challenges.https://www.mdpi.com/2076-3417/15/9/5148rough set theoryMLReductMLSpecialReductMLFuzzyRoughSetfeature selectioninterpretability |
| spellingShingle | Sami Naouali Oussama El Othmani Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models Applied Sciences rough set theory MLReduct MLSpecialReduct MLFuzzyRoughSet feature selection interpretability |
| title | Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models |
| title_full | Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models |
| title_fullStr | Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models |
| title_full_unstemmed | Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models |
| title_short | Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models |
| title_sort | rough set theory and soft computing methods for building explainable and interpretable ai ml models |
| topic | rough set theory MLReduct MLSpecialReduct MLFuzzyRoughSet feature selection interpretability |
| url | https://www.mdpi.com/2076-3417/15/9/5148 |
| work_keys_str_mv | AT saminaouali roughsettheoryandsoftcomputingmethodsforbuildingexplainableandinterpretableaimlmodels AT oussamaelothmani roughsettheoryandsoftcomputingmethodsforbuildingexplainableandinterpretableaimlmodels |