## 6 Exploration of Findings
### Case No. 2: AHC-Based Feature Selection
In the AHC-based feature set, the ANN achieved a macro-averaged F1-score of 0.993 and a weighted average F1-score of 0.994, both higher than in the RFC-based feature set (0.988 and 0.989, respectively). Similarly, the SVC improved its macro average F1-score from 0.977 to 0.991 and weighted average from 0.979 to 0.992. For complex classes such as rear defect: 5 mm, the ANN improved its F1-score from 0.977 to 0.989, and the SVC from 0.957 to 0.983. In the challenging Single-Phase Fault class, the ANN's F1-score increased from 0.967 to 0.982, and the SVC's from 0.949 to 0.974. Meanwhile, the RFC showed stable performance, with only marginal changes, which indicates its robustness but a limited benefit from AHC. Importantly, all classifiers achieve perfect scores for the no-load state, confirming excellent generalization in identifying healthy conditions. The macro and weighted averages confirm that AHC-based feature selection significantly reduces performance variability, enhances generalization, and strengthens fault discrimination, demonstrating its competitive advantage over RFC-based feature importance. Although the accuracy reduction of the RFC is minor, the marginal increase in training time indicates that the RFC still benefits from the refined feature selection process, albeit \begin{table}
\begin{tabular}{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 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline Full-load & 0.996 & 0.992 & 0.997 & 0.994 & 0.991 & 0.992 & 0.995 & 0.992 & 0.995 \\ \hline Front Defect (2 mm) & 1.000 & 0.997 & 0.997 & 0.999 & 0.993 & 0.999 & 0.998 & 0.995 \\ \hline Front Defect (5 mm) & 1.000 & 0.999 & 1.000 & 0.997 & 0.997 & 0.996 & 0.999 & 0.998 & 0.998 \\ \hline Rear Defect (2 mm) & 0.996 & 0.997 & 0.994 & 0.990 & 0.991 & 0.989 & 0.993 & 0.994 & 0.991 \\ \hline Rear Defect (5 mm) & 0.994 & 0.989 & 0.985 & 0.968 & 0.989 & 0.981 & 0.981 & 0.989 & 0.983 \\ \hline Front and Rear Defect (2 mm) & 1.000 & 1.000 & 0.999 & 0.992 & 0.997 & 0.993 & 0.996 & 0.999 & 0.996 \\ \hline Front and Rear Defect (5 mm) & 0.989 & 0.992 & 0.989 & 0.981 & 0.986 & 0.985 & 0.985 & 0.989 & 0.987 \\ \hline Single-Phase Fault & 0.992 & 0.973 & 0.964 & 0.964 & 0.991 & 0.985 & 0.968 & 0.982 & 0.974 \\ \hline micro avg & 0.996 & 0.995 & 0.994 & 0.989 & 0.994 & 0.991 & 0.992 & 0.994 & 0.992 \\ \hline macro avg & 0.994 & 0.993 & 0.992 & 0.987 & 0.993 & 0.990 & 0.991 & 0.993 & 0.991 \\ \hline weighted avg & 0.996 & 0.995 & 0.994 & 0.989 & 0.994 & 0.991 & 0.992 & 0.994 & 0.992 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance analysis of RFC, ANN, and SVC estimators using AHC-based feature selection. Figure 8: Dendrogram of hierarchical clustering revealing feature groupings based on the Spearman correlation. to a lesser extent compared to the other classifiers. The robustness of the RFC remains evident in both cases; however, the results suggest that the features introduced through AHC may be advantageous for the SVC and ANN but could pose minor challenges to the RFC's optimization. Briefly, Figure 9 provides a comparative analysis of accuracy and fitting time across the three classifiers. The analysis reveals that implementing feature selection strategies significantly enhances classifier performance. Although using all 63 features achieves a high accuracy of 99.192% for RFC, the ANN demonstrates only moderate performance at 77.338%, while the SVC records the poorest accuracy at just 15.426%. However, adopting either RF- or AHC-based feature selection significantly enhances the overall accuracy of all classifiers while simultaneously reducing fitting time. AHC-based feature selection outperforms RF for the ANN and SVC, achieving accuracies of 99.257% and 98.724%, respectively.