# 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}{l l l l} \hline \hline
**Models** & **Precision** & **Recall** & **F1-Score** \\ \hline Gaussian Process & 0.85 & 0.85 & 0.85 \\ \hline Random Forest & 0.83 & 0.83 & 0.83 \\ \hline Nearest Neighbors & 0.75 & 0.74 & 0.74 \\ \hline Linear SVM & 0.79 & 0.79 & 0.79 \\ \hline RBF SVM & 0.79 & 0.79 & 0.79 \\ \hline Neural Net & 0.74 & 0.74 & 0.74 \\ \hline Naive Bayes & 0.81 & 0.81 & 0.81 \\ \hline Decision Tree & 0.76 & 0.74 & 0.75 \\ \hline AdaBoost & 0.85 & 0.85 & 0.85 \\ \hline QDA & 0.75 & 0.74 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison classifier models without RST feature selection. Classification report for Gaussian Process

precision recall f1-score support

0 0.82 0.85 0.84 27

1 0.79 0.75 0.77 20

accuracy
0.81 47

macro avg
0.81 0.80 0.80 47

weighted avg
0.81 0.81 0.81 47

Among the models tested, the Random Forest algorithm (Figure 4) performed the best when using the MLRduct method. The use of reducts helped in reducing model complexity while maintaining high performance across all metrics (precision, recall, F1-score). Figure 2: Evaluation of Gaussian Process without RST feature selection. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Models** & **Precision** & **Recall** & **F1-Score** \\ \hline Gaussian Process & 0.85 & 0.85 & 0.85 \\ \hline Random Forest & 0.87 & 0.87 & 0.87 \\ \hline Nearest Neighbors & 0.79 & 0.79 & 0.79 \\ \hline Linear SVM & 0.85 & 0.85 & 0.85 \\ \hline RBF SVM & 0.79 & 0.79 & 0.79 \\ \hline Neural Network & 0.81 & 0.81 & 0.81 \\ \hline Naive Bayes & 0.86 & 0.85 & 0.85 \\ \hline Decision Tree & 0.79 & 0.79 & 0.79 \\ \hline AdaBoost & 0.87 & 0.86 & 0.87 \\ \hline QDA & 0.84 & 0.83 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison of classifier models with MLRduct feature selection. This analysis demonstrates that integrating the MLReduct method can lead to more efficient and effective machine learning models. By focusing on the most relevant features, we reduce noise and complexity, which results in improved model performance. #### 5.3.3 Evaluation with MLSpecialReduct Feature Selection

The **MLSpecialReduct** algorithm offers a robust approach to feature selection by identifying a minimal subset of attributes that maximizes dependency with the decision attribute. Unlike the general reduct method, which focuses on finding all possible reducts, **MLSpecialReduct** seeks an optimal set of features by iteratively evaluating the dependency of attribute subsets. This ensures that the selected subset not only retains essential information but also eliminates redundant or irrelevant features. The method enhances computational efficiency and model interpretability, making it especially valuable for large and complex datasets. Benefits of using MLSpecialReduct in evaluation include the following:

* **Optimal Attribute Selection:** MLSpecialReduct ensures that only the most influential attributes are selected, which improves the accuracy of the model. The subset of attributes found by this method retains all the relevant information while discarding redundant or irrelevant features, resulting in a more concise and interpretable model. * **Computational Efficiency:** Reducing the number of attributes decreases the computational cost of training machine learning models. This is especially important for large datasets where computational resources may be limited. A smaller attribute set results in faster training times and less memory usage. * **Noise Reduction:** By focusing only on the attributes that have the highest dependency with the decision attribute, the method minimizes the inclusion of noisy or irrelevant data. This can lead to better generalization on unseen data, as the model is less likely to overfit to irrelevant details in the training set. * **Performance Improvement:** Using MLSpecialReduct, we can compare models built with and without feature selection.