## 5 Experiment and Results
### Standard Classification Models Comparison  
This section discusses the results achieved by conducting the experiments of the proposed WOA-SVM approach in terms of accuracy and F-measure. It also compares the proposed approach with the other standard classification algorithms which are NB, K-NN, J84, and SVM. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Parameter** & **Value** \\ \hline \multirow{2}{*}{WOA} & \(a\) & 2 to 0 \\  & \(r\) & [0, 1] \\ \hline \multirow{2}{*}{MVO} & Minimum wormhole existence ratio & 0.2 \\  & Maximum wormhole existence ratio & 1 \\ \hline \multirow{4}{*}{GA} & Crossover ratio & 0.9 \\  & Mutation ratio & 0.1 \\ \cline{1-1}  & Selection mechanism & Roulette wheel \\ \hline \multirow{2}{*}{PSO} & Acceleration constants & [2.1, 2.1] \\  & Inertia w & [0.9, 0.6] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Initial parameters of the meta-heuristic algorithms. The accuracy values for the First-rule dataset for the proposed approach and the other standard algorithms can be observed in Table 4. The proposed approach has higher accuracy values than any other algorithms for all the first-rules datasets. K-NN algorithm has better accuracy values for the Data 1 and Data 2 datasets than the other basic classification algorithms, but it could not compete with the values achieved by the proposed approach, as the proposed approach is approximately 1.5% and 4.5% higher than K-NN for Data 1 and Data 2, respectively. Likewise, the NB algorithm could not compete with the values achieved by the proposed approach for Data 3, Data 4, and Data 5 datasets, although it has higher values than the other algorithms. The proposed approach is approximately 5%, 6%, and 4.5% higher than NB for Data 3, Data 4, and Data 5, respectively. In contrast, J84 has the worst accuracy values for all the first-rules datasets. The same observations can be concluded from Table 5 for the latter-rules datasets where the proposed approach has the highest accuracy values compared to the other algorithms. For these datasets, J84 has closer accuracy values to the other algorithms than the ones achieved by the first-rules datasets, but still no algorithm could achieve better values than the proposed approach. The F-measure values for the first-rules and Latter-rules datasets can be observed by Tables 6 and 7, respectively. It is observed from both tables that the proposed approach has an outstanding performance compared to the other algorithms having a recognizable higher F-measure value than the others. It has a 27-50% increase in the value of the F-measure compared to the other algorithms for the first-rules datasets. In contrast, it has a 20-31% increase in the value of the F-measure compared to the other algorithms for the latter-rules datasets. It is also observed that the SVM has the worst values compared to the others for the first-rules datasets, and NB has better values than the other standard classification algorithms for both first-rules and latter-rules datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**NB**} & \multicolumn{2}{c}{**k-NN**} & \multicolumn{2}{c}{**J84**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**WOA-SVM**} \\ \cline{2-10}  & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** \\ \hline Data 1 & 66.93\% & 3.83\% & 67.61\% & 2.71\% & 62.71\% & 3.51\% & 65.20\% & 1.31\% & **69.05\%** & 4.61\% \\ Data 2 & 66.93\% & 3.83\% & 67.61\% & 2.71\% & 62.71\% & 3.51\% & 65.20\% & 1.31\% & **72.05\%** & 2.96\% \\ Data 3 & 67.52\% & 3.99\% & 66.83\% & 2.52\% & 62.71\% & 3.51\% & 65.79\% & 1.32\% & **72.73\%** & 4.71\% \\ Data 4 & 67.52\% & 3.99\% & 66.83\% & 2.52\% & 62.71\% & 3.51\% & 65.79\% & 1.32\% & **73.64\%** & 2.58\% \\ Data 5 & 67.60\% & 3.40\% & 67.33\% & 2.62\% & 62.57\% & 3.64\% & 65.29\% & 1.57\% & **72.00\%** & 2.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy results of standard classifiers against WOA-SVM for First-rules datasets.