# Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting
## 4 Experimental Results
### Assessment with External Imagery Sources  
An extension to the assessment of the previous models was carried out, using \(D^{GLI}_{Ext_{T}St}\) and \(D^{BDI}_{Ext_{T}St}\) with a set of classes that seamlessly match the one that characterizes the former datasets applied for training, as previously explained in the materials and methods section. To that end and for the sake of contention while still ensuring some representativeness, 10 random images per class were considered.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset Split Approaches**} & \multicolumn{4}{c}{**Xception**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Nadam**} & \multicolumn{2}{c}{**SGD**} \\ \cline{2-5}  & **Acc** & **ESE** & **Acc** & **ESE** \\ \hline
**SCDIS** & 0.70 & 100 & 0.67 & 100 \\ \hline
**FSCIS** & **0.76** & 100 & **0.74** & 100 \\ \hline
**Hold out** & 0.75 & 100 & 0.69 & 100 \\ \hline
**KFCV10** & Fold 0 & 0.70 & 100 & 0.67 & 100 \\  & Fold 1 & 0.65 & 100 & 0.70 & 100 \\  & Fold 2 & 0.70 & 98 & 0.68 & 100 \\  & Fold 3 & **0.74** & 100 & **0.71** & 100 \\  & Fold 4 & 0.68 & 94 & 0.68 & 100 \\  & Fold 5 & 0.68 & 100 & 0.65 & 100 \\  & Fold 6 & 0.65 & 92 & 0.67 & 100 \\  & Fold 7 & 0.61 & 48 & 0.60 & 100 \\  & Fold 8 & 0.65 & 84 & 0.67 & 100 \\  & Fold 9 & 0.67 & 100 & 0.71 & 100 \\  & Mean KFCV10 & & **0.67** & & **0.67** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy results for models trained with datasets \(D^{GLI}_{St}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset Split Approaches**} & \multicolumn{4}{c}{**Xception**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Nadam**} & \multicolumn{2}{c}{**SGD**} \\ \cline{2-5}  & **Acc** & **ESE** & **Acc** & **ESE** \\ \hline
**SCDIS** & **0.90** & 100 & **0.86** & 100 \\ \hline
**FSCIS** & 0.88 & 100 & 0.81 & 100 \\ \hline
**Hold out** & 0.89 & 100 & 0.82 & 100 \\ \hline
**KFCV10** & Fold 0 & 0.89 & 71 & **0.86** & 100 \\  & Fold 1 & 0.88 & 100 & 0.84 & 100 \\  & Fold 2 & 0.87 & 100 & 0.82 & 100 \\  & Fold 3 & **0.90** & 100 & 0.81 & 100 \\  & Fold 4 & 0.88 & 100 & 0.82 & 100 \\  & Fold 5 & 0.88 & 100 & 0.85 & 100 \\  & Fold 6 & 0.89 & 100 & 0.83 & 100 \\  & Fold 7 & 0.89 & 100 & 0.82 & 100 \\  & Fold 8 & 0.88 & 89 & 0.84 & 100 \\  & Fold 9 & 0.88 & 100 & 0.83 & 100 \\  & Mean KFCV10 & & **0.88** & & **0.73** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy results for models trained with \(D^{BDI}_{St}\).  
The trained models were evaluated against the above-mentioned external data and the results are presented in Table 5 for \(D_{Ext\_Tst}^{BDI}\), and in Table 6 for \(D_{Ext\_Tst}^{GLI}\).  
Considering the outcomes presented in Table 5, one can infer that SCDIS- and FSCIS-based Xception/Nadam models clearly outperform HO, and, compared to KFCV10-based models, most of the accuracies are matched or surpassed (8/10). A less noticeable tendency can be observed for the FSCIS-based Xception/SGD model, which was still capable of outperforming both HO and half of the KFCV10-based models. In Table 6, the SCDIS-based model stands out, largely surpassing HO and most of the KFCV10-based models for both Xception/Nadam and Xception/SGD combinations. Less successful was the FSCIS model, which could only prevail over HO and a few KFCV10 models.