## 5 Implementation and Results
### Experiment 2
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Data Type** & \multicolumn{3}{c}{**PBC**} & \multicolumn{3}{c}{**GE**} \\ \hline  & \multicolumn{8}{c}{Grey Wolf Optimization} \\ \hline  & AUC & Recall & Precision & F1-Score & Acc & AUC & Recall & Precision & F1-Score & Acc \\ \hline NB & 97.58 & 97.48 & 97.89 & 97.14 & 97.57 & 63.34 & 43.0 & 46.66 & 45.0 & 60.0 \\ \hline KNN & 96.89 & 96.79 & 97.23 & 96.42 & 96.88 & 63.33 & 72.0 & 58.33 & 100 & 62.5 \\ \hline SVM & 99.66 & 99.67 & 99.33 & 100 & 99.69 & 99.34 & 96.0 & 100 & 92.66 & 99.33 \\ \hline DT & 98.29 & 98.16 & 98.54 & 97.85 & 98.27 & 80.0 & 83.33 & 81.66 & 95.0 & 85.0 \\ \hline \multicolumn{8}{c}{Flower Pollination Algorithm} \\ \hline NB & 94.88 & 94.61 & 94.56 & 95.04 & 94.87 & 70.0 & 59.66 & 61.66 & 60.0 & 61.0 \\ \hline KNN & 95.52 & 95.33 & 95.10 & 95.76 & 95.54 & 60.0 & 70.33 & 56.66 & 100 & 60.0 \\ \hline SVM & 99.56 & 99.65 & 99.33 & 100 & 99.66 & 96.67 & 97.14 & 95.0 & 100 & 96.67 \\ \hline DT & 96.21 & 95.98 & 97.10 & 95.0 & 96.16 & 76.66 & 69.33 & 68.33 & 75.0 & 72.5 \\ \hline \multicolumn{8}{c}{Bat Algorithm} \\ \hline NB & 97.60 & 97.50 & 98.61 & 96.47 & 97.57 & 63.33 & 43.33 & 50.0 & 40.0 & 57.5 \\ \hline KNN & 93.14 & 92.82 & 93.63 & 92.85 & 93.09 & 56.66 & 68.33 & 53.33 & 100 & 55.0 \\ \hline SVM & 98.97 & 98.78 & 98.01 & 100 & 99.0 & 97.43 & 97.33 & 97.33 & 97.33 & 97.34 \\ \hline DT & 96.22 & 95.83 & 97.75 & 94.22 & 96.11 & 89.99 & 91.66 & 93.33 & 95.0 & 92.0 \\ \hline \multicolumn{8}{c}{Artificial Bee Colony} \\ \hline NB & 95.54 & 95.02 & 97.90 & 92.85 & 95.42 & 56.66 & 48.33 & 55.0 & 45.0 & 52.5 \\ \hline KNN & 98.27 & 98.22 & 97.90 & 98.57 & 98.28 & 53.33 & 66.67 & 51.66 & 100 & 52.5 \\ \hline SVM & 98.63 & 98.61 & 98.08 & 99.28 & 98.66 & 96.66 & 96.0 & 100 & 93.33 & 96.67 \\ \hline DT & 97.73 & 97.89 & 98.06 & 97.85 & 97.92 & 93.33 & 94.66 & 91.66 & 100 & 92.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Second Experiment Results. Figure 3: Comparison of accuracy between proposed models on PBC dataset. with the SVM-BA model (97.34%) compared to the BA-DT model (89.99%), followed by the BA-NB model (63.33%), and BA-KNN gave the lowest accuracy of 56.66%. For ABC-based models, the ABC-SVM model gave the highest accuracy (96.66%) compared with the ABC-DT model (93.33%) and ABC-NB (56.66%), while the lowest accuracy was 53.33% for the ABC-KNN model. According to the obtained results, GWO-SVM had the best classification performance on the GE dataset compared to the remaining classifiers. In general, we can say that the proposed models achieved a good predictive performance on the two datasets. For the PBC dataset, the SVM and DT classifiers had a better performance with the four optimized wrapper methods. GWO-SVM and FPA-SVM were the best models with highest accuracies of 99.66% and 99.56%, respectively. As for the GE dataset, the SVM classifier was better with the four optimized wrapper methods than the other classifiers. GWO-SVM and BA-SVM were the best models with the highest accuracies of 99.34% and 97.43%, respectively. Figure 5 presents the F1 score results of the 16 proposed models on PBC dataset. The SVM classifier gave the highest results with the GWO, FPA, BA, and ABC-based models (99.67%, 99.65%, 98.78%, and 98.61%, respectively) compared to the other classifiers. On the other hand, the BA-KNN model gave the lowest result (92.82%). Figure 6 presents the F1 score results of the 16 proposed models on the GE dataset. The SVM classifier gave the highest results with GWO, FPA, BA, and ABC-based models (96.0%, 97.14%, 97.33%, and 96.0%, respectively) compared to the other classifiers. On the other hand, the BA-NB model gave the lowest result (43.33%). Figure 4: Comparison of accuracy between proposed models on the GE dataset. Figure 5: Comparison of F1 score results between proposed models on PBC dataset. Figure 7 shows the graphical representation of the ROC curves for all four classifiers in each wrapper selection method on the PBC dataset.