## 6 Exploration of Findings
### Case No. 2: AHC-Based Feature Selection  
The dendrogram based on the Spearman correlation matrix is presented in Figure 8 to evaluate the interrelationships among all features and to assess their influence on classifier performance. The dendrogram in Figure 8 highlights how features are inherently grouped into four distinct clusters. Each cluster shares common information among the features it contains. Additionally, Figure 8 is color-coded: features identified as important by the RF algorithm are colored blue, features deemed important by both the RF and the suggested algorithm are highlighted in red, and those identified exclusively by the proposed algorithm are marked in purple. Notably, RF-based methods tend to select features with high correlations or shorter distances in hierarchical clustering terms. However, AHC-based feature selection selects a representative set of features from each cluster. The suggested approach ensures that all clusters are represented and considered. Such an approach should better preserve unique information while effectively mitigating issues of multicollinearity. The performance of the features selected by the AHC algorithm is reported in Table 4, in which the same performance metrics as those used in Case No. 1 are employed. As reported in Table 4, all three classifiers exhibited significant improvements in their performance. Notably, the accuracy of the SVC increased by approximately 2.03%, increasing from 96.75% in Case No. 1 to 98.724%, with the fitting time reduced to just 4.32 s. This enhancement underscores the representativeness of the features selected by AHC, which is allowing the SVC to generalize more effectively across diverse operational \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Metrics** & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1-Score**} \\ \hline
**Classifier** & **RFC** & **ANN** & **SVC** & **RFC** & **ANN** & **SVC** & **RFC** & **ANN** & **SVC** \\ \hline No-load & 1.000 & 0.997 & 0.999 & 1.000 & 1.000 & 1.000 & 1.000 & 0.999 & 0.999 \\ \hline Full-load & 0.992 & 0.980 & 0.974 & 0.995 & 0.995 & 0.995 & 0.994 & 0.987 & 0.984 \\ \hline Front Defect (2 mm) & 1.000 & 0.997 & 0.996 & 0.996 & 1.000 & 0.993 & 0.998 & 0.999 & 0.994 \\ \hline Front Defect (5 mm) & 1.000 & 1.000 & 0.997 & 0.997 & 0.997 & 0.997 & 0.999 & 0.999 & 0.997 \\ \hline Rear Defect (2 mm) & 0.997 & 0.996 & 0.940 & 0.987 & 0.991 & 0.920 & 0.992 & 0.994 & 0.930 \\ \hline Rear Defect (5 mm) & 0.997 & 0.959 & 0.947 & 0.992 & 0.996 & 0.968 & 0.994 & 0.977 & 0.957 \\ \hline Front and Rear Defect (2 mm) & 1.000 & 0.997 & 0.996 & 0.996 & 0.999 & 1.000 & 0.998 & 0.998 & 0.998 \\ \hline Front and Rear Defect (5 mm) & 0.999 & 0.997 & 0.996 & 0.983 & 0.947 & 0.964 & 0.991 & 0.971 & 0.979 \\ \hline Single-Phase Fault & 0.976 & 0.990 & 0.963 & 0.976 & 0.945 & 0.936 & 0.976 & 0.967 & 0.949 \\ \hline micro avg & 0.997 & 0.990 & 0.980 & 0.992 & 0.988 & 0.978 & 0.995 & 0.989 & 0.979 \\ \hline macro avg & 0.996 & 0.990 & 0.978 & 0.991 & 0.986 & 0.975 & 0.993 & 0.988 & 0.977 \\ \hline weighted avg & 0.997 & 0.990 & 0.980 & 0.992 & 0.988 & 0.978 & 0.995 & 0.989 & 0.979 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance analysis of RFC, ANN, and SVC estimators using RF-based feature selection. conditions. Similarly, the ANN's average accuracy increased to 99.257%, with a fitting time of 14.33 s, compared to 98.68% achieved in Case No. 1. The improvement in accuracy indicates a more efficient learning process, albeit with a slight increase in training time compared to the RF-based selection. Conversely, the RFC exhibited a slight decline in its average accuracy, dropping to 98.869%, while achieving a fitting time of just 0.74 s.  
Furthermore, the application of AHC for feature selection resulted in measurable performance improvements, particularly for the ANN and SVC.