## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Extractor**} & **Optimizer** & \multicolumn{4}{c}{**Hyperparameter Tuning**} & **Remaining** & **Accuracy** & **F1-Score** & **Geometric Mean** \\  & **Approach** & **C** & **Degree** & **Coof0** & **Tolerance** & **Feature Parameter** & **Features (\%)** & **(\%)** & **(\%)** & **(\%)** & **Geometric Mean** \\ \hline  & BSMO & & & & Default parameter & & 28.91 & 93.94 & 93.33 & 0.8172022148770768 \\ \hline  & BPSO & & & Default parameter & & 24.22 & 92.42 & 91.65 & 0.836874399178275 \\ \cline{2-10} LBP & HSMO & 4050.0 & 4.0 & 3.0 & 0.012 & Radius: 14.0 & 26.56 & 97.62 & 97.49 & 0.71692127999999 \\ \cline{2-10}  & HPSO & 3880.0 & 7.0 & 3.0 & 0.002 & Radius: 14.0 & 23.44 & 95.89 & 95.67 & 0.73413384 \\ \hline  & BSMO & & & Default parameter & & 35.16 & 64.94 & 64.63 & 0.6488998073662836 \\ \cline{2-10}  & BPSO & & & Default parameter & & 32.81 & 63.64 & 0.63 & 0.635091734189535 \\ \cline{2-10} Gabor Filters & HSMO & 1820.0 & 6.0 & 1.4 & 0.0004 & size: 180.0 & & & & \\  & & & & & sigma: 6.9 & & & & & \\  & & & & & theta: 2.0 & & 39.06 & 85.5 & 83.78 & 0.7218289271011518 \\  & & & & & landmark: 8.0 & & & & & \\  & & & & & gamma: 249.0 & & & & & \\ \cline{2-10}  & HPSO & 7110.0 & 6.0 & 1.5 & 0.011 & size: 5.2 & & & & & \\  & & & & & theta: 2.0 & 33.59 & 83.33 & 81.97 & 0.7439049199998613 \\  & & & & & landmark: 4.5 & & & & & \\  & & & & & gamma: 348.5 & & & & & \\ \hline  & BSMO & & & & Default parameter & & 31.25 & 93.29 & 92.91 & 0.8008550118467137 \\ \cline{2-10}  & BPSO & & & & Default parameter & & 32.81 & 91.34 & 90.99 & 0.78339669873905 \\ \cline{2-10} HOG & HSMO & 410.0 & 5.0 & 2.0 & 0.022 & Orientations: 12.0 & & & & \\  & & & & & Flocks per cell: 2.0 & 25.78 & 98.27 & 98.15 & 0.8540257256078414 \\  & & & & & Cells per block: 3.0 & & & & \\ \cline{2-10}  & HPSO & 730.0 & 13.0 & 1.5 & 0.033 & Orientations: 140 & & & & \\  & & & & & Flocks per cell: 3.0 & 25.78 & 95.02 & 94.71 & 0.839/847581374646 \\ \cline{2-10}  & & & & & Cells per block: 5.0 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of optimization process using diverse feature extraction methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Feature** & **Optimizer** & \multicolumn{4}{c}{**Hyperparameter Tuning**} & **Remaining** & **Accuracy** & **F1-Score** & **Geometric Mean** \\
**Extractor** & **Approach** & **C** & **Degree** & **Coof0** & **Tolerance** & **Feature Parameter** & **Features (\%)** & **(\%)** & **(\%)** & **Geometric Mean** \\ \hline  & BSMO & & & Default parameter & & 32.03 & 88.31 & 88.31 & 0.74753545462249 \\ \cline{2-10}  & BPSO & & & Default parameter & & 39.06 & 89.39 & 89.28 & 0.7380668397916275 \\ \cline{2-10}  & HSMO & 2380.0 & 6.0 & 1.5 & 0.009 & Aperture size: 37.0 & 37.5 & 91.99 & 91.04 & 0.7582463320056353 \\ \cline{2-10}  & HPSO & 1770.0 & 4.0 & 4.5 & 0.006 & Aperture size: 16.0 & & & \\  & & & & & Aperture size: 0.0 & 32.03 & 85.71 & 83.33 & 0.7632633031922863 \\ \cline{2-10}  & HPSO & 1770.0 & 4.0 & 4.5 & 0.006 & Aperture size: 4.0 & 32.03 & 85.71 & 83.33 & 0.763263031922863 \\ \cline{2-10}  & HPSO & & & & & Aperture size: 12.0 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3. _Cont._Additionally, in order to include another layer of explainability at this stage, SHAP was used to analyze the distribution of feature importance of selected features across the dataset. This step allowed us to assess whether the use of metaheuristic algorithms had an impact on the importance distribution of selected features. Figure 11 indicates the respective feature importance charts of LBP features using a BSMO, BPSO, HSMO, and HPSO optimizer. A visible difference can be seen, where the distribution of remaining features in each metaheuristic algorithm tends to move towards greater importance scores. The most notable change occurs for HSMO and HPSO optimizers.