## 6 Experimental Results and Simulations
### Comparison with State-of-the-Art Approaches  
To further validate the results of the proposed method, it is compared with nine state-of-the-art methods in [89] using the G-mean measure (G-mean is the reported measure in this study). Considering the results in Table 18, the superiority and competitiveness of the proposed method is evident again. Furthermore, we compared the proposed method with the best results achieved in the study of [PERSON] and [PERSON] [14] and [PERSON] et al. [15] in terms of AUC measure. As in Table 19, it can be seen that our proposed approach achieved the best AUC rates compared to results presented in previous studies on the same datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Sequence \#** & **Feature** & **Number of Selections** & **Ratio** \\ \hline \multirow{4}{*}{Data1} & 31 & G2 & 10 & 100\% \\  & 13 & Travel-time & 2 & 20\% \\  & 30 & absences & 2 & 20\% \\  & 31 & G1 & 2 & 20\% \\  & 10 & Fjob & 1 & 10\% \\ \hline \multirow{4}{*}{Data2} & 1 & instr & 10 & 100\% \\  & 3 & attendance & 10 & 100\% \\ \cline{1-1}  & 4 & difficulty & 10 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The most relevant features selected by EWOA-V4 based on the total number of selections over 10 independent runs. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Dataset** & **Metric** & & **EWOA-S2** & **EWOA-V4** & **BHHO** & **BCSA** & **BGOA** & **BPSO** & **BGWO** & **BBA** & **GA** & **BALO** \\ \hline \multirow{4}{*}{Data1} & AUC & AVG & 0.91683 & 0.91573 & 0.89627 & 0.89053 & 0.89762 & 0.90332 & 0.90508 & 0.86580 & 0.89977 & 0.89124 \\  & AUC & 0.00133 & 0.00105 & 0.00629 & 0.00760 & 0.00939 & 0.00624 & 0.00483 & 0.06794 & 0.00734 & 0.00486 \\  & Features & AVG & 2.2 & 1.7 & 13.1 & 14.5 & 13.2 & 10.5 & 5.5 & 14.4 & 10.8 & 22.5 \\  & STD & 0.91894 & 1.25167 & 2.51440 & 2.22361 & 2.52982 & 2.27303 & 1.84089 & 2.63312 & 2.82056 & 5.25463 \\  & Fitness & AVG & 0.08302 & 0.08396 & 0.09518 & 0.09882 & 0.09354 & 0.09195 & 0.08562 & 0.09850 & 0.09693 & 0.10179 \\  & STD & 0.00142 & 0.00129 & 0.00222 & 0.00291 & 0.00261 & 0.00178 & 0.00217 & 0.00196 & 0.00285 & 0.00137 \\ \hline \multirow{4}{*}{Data2} & AUC & AVG & 0.65468 & 0.65569 & 0.63839 & 0.63735 & 0.63671 & 0.64021 & 0.63917 & 0.61790 & 0.63980 & 0.63752 \\  & STD & 0.00232 & 0.00056 & 0.00355 & 0.00409 & 0.00502 & 0.00315 & 0.00246 & 0.02016 & 0.00240 & 0.00444 \\  & AVG & 3.9 & 3 & 19.2 & 16.8 & 18.4 & 15.7 & 11.8 & 17 & 15 & 26.1 \\  & STD & 2.84605 & 0.00000 & 3.58391 & 19.9218 & 2.59058 & 2.21359 & 2.74064 & 1.33333 & 1.63299 & 3.21282 \\  & Fitness & AVG & 0.34308 & 0.34180 & 0.35585 & 0.35707 & 0.35651 & 0.35460 & 0.35096 & 0.35946 & 0.35853 & 0.35892 \\  & STD & 0.00318 & 0.00055 & 0.00124 & 0.00218 & 0.00137 & 0.00128 & 0.00124 & 0.00342 & 0.00111 & 0.00092 \\ \hline \multicolumn{2}{c}{Overall rank (F-Test)} & 1.67 & **1.33** & 6.5 & 8 & 6.83 & 4 & 3.33 & 8.83 & 5.5 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Comparison of the proposed approaches with other well-regarded algorithms in terms of AUC, selected features, and fitness values. Figure 16: Convergence curves for all compared algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Data1** & **Data2** \\ \hline our approach & **0.9146** & 0.6548 \\ Method1-Opt1 & 0.7495 & 0.7256 \\ Method1-Opt2 & 0.7486 & 0.7276 \\ Method2-Opt1 & 0.7488 & 0.7267 \\ Method2-Opt2 & 0.7478 & 0.7259 \\ Original-RVFL & 0.7113 & 0.6887 \\ Imp-RVFL-KDE & 0.7242 & 0.7068 \\ Imp-RVFL-MCC & 0.7257 & 0.7072 \\ Imp-RVFL-Opt1 & 0.7198 & 0.7158 \\ Imp-RVFL-Opt2 & 0.7217 & 0.7123 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Validation of our proposed method with the proposed methods by [89] in terms of G-mean measure. Taken together, the experiments and comparative results demonstrated the merits of the proposed WOA methods. The superiority of the proposed methods are due the several reasons. Firstly, the exploitation of WOA was improved using the SCA algorithm.