# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 4 Methodology
### Classification
#### 4.2.2 MLSpecialReduct
Error bars show standard deviation across 10 folds. MLSpecialReduct maintains consistent superiority on both datasets, with marginal performance differences attributable to sample size (303 vs. 1000) and feature distribution variations. * Bonferroni-corrected \(p\)-values remained significant (\(p_{adj}<0.001\)). **Clinical/Engineering Implications**: The combination of high accuracy (0.99) and low variability (\(\sigma\) = 0.01) makes MLSpecialReduct particularly suitable for the following:

* High-stakes medical diagnostics where false negatives are critical;
* Real-time systems requiring predictable performance. ### Comparison with State of the Art

In this section, we compare the performance of our proposed algorithms for feature selection like MLReduct and MLSpecialReduct with state-of-the-art techniques in feature selection and machine learning. Our goal is to demonstrate the superiority of our methods in terms of accuracy, interpretability, and computational efficiency. #### 5.7.1 Comparison with Traditional Feature Selection Methods

Traditional feature selection methods, such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and MLVarianceThreshold, have been widely adopted in machine learning workflows as established baselines. However, these methods often struggle with high-dimensional datasets and fail to capture complex relationships between attributes, particularly under uncertainty and noise. Table 15 provides a comparative analysis of our RST-based methods against these traditional approaches. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy** & **F1-Score** \\ \hline MLSpecialReduct & \(0.99\pm 0.01\) & \(0.98\pm 0.01\) \\ \hline MLReduct & \(0.87\pm 0.02\) & \(0.86\pm 0.02\) \\ \hline MLVarianceThreshold & \(0.77\pm 0.03\) & \(0.76\pm 0.03\) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Cross-validation performance (10 folds). Figure 10: Accuracy distribution across 10-fold cross-validation. The box represents the interquartile range (IQR: Q1–Q3), the horizontal line indicates the median, and whiskers extend to 1.5 \(\times\) IQR. Outliers are shown as individual points. MLSpecialReduct demonstrates both superior accuracy and consistency across folds. As shown in Table 15, our RST-based methods consistently outperform traditional techniques. The MLSpecialReduct method, in particular, achieves a peak F1-score of 0.98, significantly surpassing the best-performing traditional method (RFE with an F1-score of 0.79). This highlights the ability of RST-based methods to identify and retain the most relevant features, leading to superior model performance under challenging conditions. #### 5.7.2 Comparison with State-of-the-Art Feature Selection Techniques

Our RST-based methods were benchmarked against seven categories of modern feature selectors, as shown in Table 16. The analysis reveals three key advantages:

**Key Findings**:

* **Performance Superiority**:
* 11% higher accuracy than the best non-RST method (GA + SVM);
* 12% improvement over prior RST work (Tri-Level Reduct);
* Consistent F1-score advantage (\(\Delta\geq 0.10\)). * **Efficiency Gains**:
* 3\(\times\) faster than comparable RST methods;
* Real-time capable (<70 s) for clinical applications. * **Interpretability**:
* Only method achieving both \"High\" interpretability and >0.95 accuracy;
* Generates human-readable rules. **Limitations**: Our approach shows marginally higher runtime than filter methods (e.g., Mutual Info) but provides significantly better accuracy (+17%) and explainability. This trade-off is justified in medical applications where both performance and interpretability are critical.