## 6 Experimental Results and Simulations
### Preliminary Experiments  
The first series of experiments were employed to assess the performance of five different classifiers (i.e., kNN, DT, LDA, LB, and NB) and determine which is the most applicable approach that fits both case studies here in this work. The preliminary experiments were divided into two categories, the first experiments are to classify the datasets without any preprocessing, while the second experiments are to examine the performance of classifiers with the resampling method using different balancing ratios. Table 5 explores the performance of the classifiers without resampling and without FS using four measures (i.e., TPR, TNR, AUC, and accuracy), while Table 6 explores the performance of each classifier with different balancing ratios without FS. Inspecting AUC values in Table 5, it is evident that the LB classifier outperforms all other classifiers with excellent performance for Data 1 (i.e., AUC = 0.8463) and poor performance for Data2 (i.e., AUC = 0.5982). The reported results in Table 6 after employing a re-sampling process with different oversampling ratios show that the KNN classifier has excellent performance (i.e., AUC = 0.8600) for Data 1 with oversampling ratio equals to 0.4. In contrast, the LDA classifier shows a good performance (i.e., AUC = 0.6352) for Data2 \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Predicted Class**} \\ \hline  & \multicolumn{2}{c}{Class = Yes} & \multicolumn{2}{c}{Class = No} \\ \cline{2-4}
**Actual Class** & Class = Yes & True Positive (TP) & False Negative (FN) \\ \cline{2-4}  & Class = No & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The confusion matrix. with oversampling ratio equals to 1.0. Table 7 compares the performance of all classifiers based on three criteria (i.e., TPR, TNR, and AUC) values with and without oversampling. It is evident that the oversampling method for both cases will improve the performance of all classifiers. The performance of LDA dominates all other classifiers with re-sampling. As a result, we will adopt the LDA as a primary classifier for evaluating the performance of the proposed EWOA. \begin{table}
\begin{tabular}{l c} \hline \hline
**Configuration** & **Value** \\ \hline \multicolumn{2}{c}{**Fitness function**} \\ \hline \(\alpha\) & 0.99 \\ \(\beta\) & 0.01 \\ \hline \multicolumn{2}{c}{**common config.**} \\ \hline No. runs & 10 \\ Population size & 20 \\ No. iterations & 70 \\ Dimension & \#features \\ K for cross validation & 5 \\ \hline \multicolumn{2}{c}{**specific config.**} \\ \hline \(G_{0}\) (for BGSA) & 10 \\ \(a\) (Convergence constant for bGWO) & from 2 to 0 \\ \(Q_{min}\) Minimum frequency (for BBA) & 0 \\ \(Q_{max}\) Maximum frequency (for BBA) & 2 \\ \(A\) Loudness (for BBA) & 0.5 \\ \(r\) Pulse rate (for BBA) & 0.5 \\ \(a\) (Convergence constant for WOA) & from 2 to 0 \\ \(E\) (for HHO) & from 2 to 0 \\ \(\omega\) (for PSO) & from 0.9 to 0.2 \\ \(c_{1}\) and \(c_{2}\) (for PSO & 2 \\ GA selection & Roulette Wheel Selection \\ Probability of mutation in GA & 0.01 \\ Probability of crossover in GA & 0.9 \\ elite size (in GA) & 2 \\ \(c\) (for BGOA) & from 0.01 to 0.00004 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The detailed parameters settings. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Classifier** & **TPR** & **TNR** & **AUC** & **Accuracy** \\ \hline \multirow{4}{*}{Data1} & KNN & 0.9649 & 0.6824 & 0.8236 & 0.9026 \\  & DT & 0.9329 & 0.7276 & 0.8303 & 0.8877 \\  & LDA & 0.9625 & 0.6822 & 0.8223 & 0.9007 \\  & LB & 0.9482 & 0.7443 & 0.8463 & 0.9033 \\  & NB & 0.9846 & 0.4678 & 0.7262 & 0.8708 \\ \hline \multirow{4}{*}{Data2} & KNN & 0.2380 & 0.9303 & 0.5842 & 0.8220 \\  & DT & 0.2812 & 0.9017 & 0.5914 & 0.8046 \\ \cline{1-1}  & LDA & 0.0280 & 0.9930 & 0.5105 & 0.8420 \\ \cline{1-1}  & LB & 0.2471 & 0.9493 & 0.5982 & 0.8394 \\ \cline{1-1}  & NB & 0.0570 & 0.9824 & 0.5197 & 0.8376 \\ \hline \hline \end{tabular}
\end{table}