# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 4 Methodology
### Classification
#### 4.2.2 MLSpecialReduct
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Type** & **Accuracy** & **F1-Score** & **Interpretability** & **Time (s)** \\ \hline Tri-Level Reduct [15] & RST & 0.87 & 0.86 & High & 210 \\ \hline Mutual Info [39] & Filter & 0.82 & 0.81 & Medium & 45 \\ \hline L1-SVM [25] & Embedded & 0.85 & 0.84 & Low & 120 \\ \hline XGBost with SHAP [40] & Ensemble with Interpretability & 0.85 & 0.83 & High (with biases) & 120 \\ \hline DFS [41] & Deep & 0.85 & 0.85 & Low & 200 \\ \hline GA+SVM [42] & Hybrid & 0.88 & 0.88 & Medium & 150 \\ \hline MLSpecialReduct (Ours) & RST & **0.99** & **0.98** & **High** & **68** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comprehensive benchmark of feature selection methods. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Precision** & **Recall** & **F1-Score** \\ \hline PCA & 0.78 & 0.77 & 0.77 \\ \hline RFE & 0.79 & 0.79 & 0.79 \\ \hline MLVarianceThreshold & 0.72–0.77 & 0.72–0.77 & 0.72–0.77 \\ \hline MLRduct & 0.87 & 0.87 & 0.87 \\ \hline MLFuzzyRoughSet & 0.83 & 0.83 & 0.83 \\ \hline MLSpecialReduct & 0.99 & 0.99 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison of RST-based methods with traditional feature selection techniques. #### 5.7.3 Comparison with State-of-the-Art Classifiers

In addition to feature selection, we compare the performance of classifiers trained using RST-based feature selection methods against state-of-the-art classifiers. Table 17 presents the results of this comparison. As shown in Table 17, the Random Forest classifier trained using the MLSpecialReduct method achieves an F1-score of 0.98, outperforming state-of-the-art classifiers such as XGBoost (F1-score of 0.90) and LightGBM (F1-score of 0.91). This demonstrates the potential of RST-based feature selection to enhance the performance of even the most advanced classifiers. #### 5.7.4 Discussion

The results of our comparisons highlight the significant advantages of RST-based feature selection methods over traditional and state-of-the-art techniques. The MLSpecialReduct method, in particular, stands out for its ability to achieve high accuracy while maintaining interpretability and computational efficiency. These advantages make RST-based methods particularly well suited for real-world applications, such as healthcare diagnostics and IoT systems, where both performance and interpretability are critical. Furthermore, our findings suggest that RST-based methods can serve as a foundation for future research in explainable AI (XAI) and hybrid feature selection models. By combining the strengths of RST with other advanced techniques, it may be possible to develop even more powerful and interpretable machine learning workflows. Our study demonstrates that the MLSpecialReduct algorithm represents a significant advancement over existing state-of-the-art techniques. These methods not only improve model performance but also enhance interpretability and computational efficiency, making them a valuable tool for modern data-driven applications. ## 6 Conclusions

### General Conclusions

This study assessed the impact of Rough Set Theory (RST)-based feature selection methods--integrated with preprocessing steps like encoding, normalization, discretization, and outlier removal--on machine learning classifier performance. Key findings include the following:

* **MLSpecialReduct**: Achieved a peak Random Forest accuracy of **0.99**, demonstrating its superior ability to minimize attribute sets while maximizing predictive power. * **MLReduct**: Boosted Random Forest accuracy to 0.87, confirming its effectiveness as a foundational RST method for feature selection. * **MLFuzzyRoughSet**: Improved Naive Bayes and Random Forest accuracies to 0.83, showcasing its robustness in handling uncertainty and imprecision.