# Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting
## 5 Discussion, Summary, and Conclusions  
In this section, the main findings and contributions of this work are summarized. This research work hypothesizes feature-aware data splitting as a strategy to enhance the performance of classification models in a timelier manner, at the expense of traditional techniques, such as KFCV and HO. Therefore, datasets of two distinct contexts were considered for the sake of condition variability: one related to bridge defects and the other associated with grapevine leaves, for phenotyping distinction. Regarding the proposed splitting approaches, the first one employs a feature-oriented clustering-based splitting technique, with a selection method based on the centroid distance of each cluster to the global centroid--FCDIS. The second approach revolves around sorting and picking images at the feature space level, based on their distance to the global \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{**SCDIS**} & \multicolumn{4}{c}{**FSCIS**} & \multicolumn{4}{c}{**HO**} & \multicolumn{4}{c}{**KFCV10—Fold3**} \\ \cline{2-13}  & \(\mu\)**Acc** & \(\mu\)**IoU** & **uGCS** & \(\mu\)**Dis** & \(\mu\)**Acc** & \(\mu\)**IoU** & **uGCS** & \(\mu\)**Dis** & \(\mu\)**Acc** & \(\mu\)**IoU** & **uGCS** & \(\mu\)**Dis** & \(\mu\)**Acc** & \(\mu\)**IoU** & **uGCS** & \(\mu\)**Dis** \\ \hline _Codega_ & 0.00 & 0.12 & 4393 & 85 & 0.00 & 0.13 & 4880 & 82 & 0.00 & 0.10 & 3541 & 87 & 0.00 & 0.11 & 4139 & 86 \\ _Moscatel_ & 0.90 & 0.173 & 7349 & 69 & 0.50 & 0.16 & 6311 & 72 & 0.70 & 0.14 & 5396 & 75 & 0.00 & 0.15 & 4996 & 70 \\ _Rabietoa_ & 0.60 & 0.19 & 7678 & 67 & 0.80 & 0.19 & 7478 & 70 & 0.50 & 0.15 & 6099 & 76 & 0.70 & 0.15 & 6000 & 72 \\ _Tinta Roira_ & 0.20 & 0.24 & 8231 & 63 & 0.10 & 0.23 & 7731 & 62 & 0.10 & 0.24 & 7956 & 62 & 0.00 & 0.24 & 8526 & 62 \\ _Tinto Cao_ & 0.00 & 0.21 & 7961 & 58 & 0.00 & 0.23 & 8911 & 58 & 0.00 & 0.16 & 6467 & 62 & 0.10 & 0.15 & 6415 & 65 \\ _Touriga Nacional_ & 0.20 & 0.23 & 7707 & 67 & 0.10 & 0.21 & 7652 & 70 & 0.00 & 0.28 & 8784 & 63 & 0.00 & 0.22 & 7915 & 70 \\ \hline
**Total mean** & **0.38** & **0.19** & 7220 & 68 & **0.25** & **0.19** & 7160 & 69 & 0.22 & 0.18 & 6374 & 71 & 0.13 & 0.17 & 6332 & 71 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction results upon the \(D_{Ext\_Tst}^{GLI}\) for Xception Nadam. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{**SCDIS**} & \multicolumn{4}{c}{**FSCIS**} & \multicolumn{4}{c}{**HO**} & \multicolumn{4}{c}{**KFCV10—Fold3**} \\ \cline{2-13}  & \(\mu\)**Acc** & \(  
centroid--FSCIS. The main goal is to compare both of these methods with conventional techniques, namely, HO and KFCV with 10 folds. For training the classification models, the Xception architecture was utilized with a learning rate of \(10^{-3}\), and optimizers including Nadam and SGD, and the maximum number of training epochs was set to 100. In most of the experiments, the proposed FCDIS and FSCIS methods outperformed the traditional approaches. Furthermore, the accuracy-loss plots illustrated a more stable training process with the proposed methods, characterized by an apparent reduction in abrupt fluctuations associated with these metrics across training sessions. To assess the trained classifiers, external datasets were considered for a Grad-CAM-based evaluation, while IoU was used as another metric for performance comparison. The results pointed out that the models trained with the proposed methods had a consistently higher capacity in identifying the relevant areas of the images, highlighting them as more effective compared to the ones trained with traditional splitting techniques.