## 3 Results
### Effects of Feature Selection on Classification Performance  
Feature selection techniques can often enhance classification performance by identifying the most relevant features for the task. In this study, we employed RFECV, an iterative feature selection method that recursively removes features and builds models on the remaining features, using model accuracy to identify the most predictive feature combinations. We implemented RFECV with a minimum threshold of 150 features to balance dimensionality reduction with maintaining sufficient complex features for effective deep learning pattern extraction.  
We evaluated the impact of RFECV by comparing model performance with and without feature selection. When applying RFECV to the combined wavelet transformation and Gabor-like filtering features, both architectures showed improved performance: ResNet1D achieved an AUC of 0.8223 \(\pm\) 0.0722 and ACC of 0.7541 \(\pm\) 0.0264, while CNN1D yielded an AUC of 0.8230 \(\pm\) 0.0177 and ACC of 0.7629 \(\pm\) 0.0215. However, when applying RFECV to wavelet features alone, both models showed slightly decreased performance. ResNet1D achieved an AUC of 0.8152 \(\pm\) 0.0272 and ACC of 0.7410 \(\pm\) 0.0315, while CNN1D achieved an AUC of 0.8134 \(\pm\) 0.0256 and ACC of 0.7523 \(\pm\) 0.0339.  
For configurations using only Gabor-like filtering, RFECV had no impact as the number of features (96 features) was below our preset minimum threshold of 150 features, resulting in identical performance with and without RFECV. When applying RFECV to hyperspectral data without feature extraction methods, we observed improved performance in both architectures: ResNet1D showed substantial improvement with an AUC of 0.6980 \(\pm\) 0.0351 and ACC of 0.6899 \(\pm\) 0.0429, while CNN1D showed modest gains with an AUC of 0.6643 \(\pm\) 0.0656 and ACC of 0.6690 \(\pm\) 0.0540.  
These results suggest that RFECV's effectiveness varies depending on the feature extraction method employed. While RFECV improved performance when applied to combined features (wavelet and Gabor-like filtering features) or hyperspectral data without feature extraction, it slightly decreased performance when applied to wavelet features alone. This indicates that wavelet transformation may already provide an optimal feature set for classification, and further feature selection could potentially remove informative features. The similar performance patterns observed across both ResNet1D and CNN1D architectures further validate these findings. Notably, across all experimental configurations, the highest classification performance was achieved using ResNet1D with wavelet features alone, without applying RFECV. The complete performance metrics are summarised in Table 3. The overall performance metrics of all experiments are visualised by ROC curves and shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**ResNet1D with**} & \multicolumn{3}{c}{**CNN1D with**} \\ \multicolumn{2}{c}{**Combined Features**} & \multicolumn{1}{c}{**ResNet1D with**} & \multicolumn{1}{c}{**Combined Features**} & \multicolumn{1}{c}{**CNN1D with Wavelet**} \\ \multicolumn{2}{c}{**(Wavelet**} & \multicolumn{1}{c}{**Wavelet Transformation**} & \multicolumn{1}{c}{**Wavelet Transformation and**} & \multicolumn{1}{c}{**Transformation and**} & \multicolumn{1}{c}{**Transformation and RFECV**} \\ \multicolumn{2}{c}{**and RFECV**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Gabor-like Filtering**} & \multicolumn{1}{c}{**and RFECV**} & \\ \hline AUC & 0.8223 \(\pm\) 0.0327 & 0.8152 \(\pm\) 0.0272 & 0.8230 \(\pm\) 0.0177 & 0.8134 \(\pm\) 0.0256 \\ ACC & 0.7541 \(\pm\) 0.0264 & 0.7410 \(\pm\) 0.0315 & 0.7629 \(\pm\) 0.0215 & 0.7523 \(\pm\) 0.0339 \\ \hline \hline \end{tab