## 5 Experiment and Results
### Standard Classification Models Comparison
\begin{table}
\begin{tabular}{c 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** & **Avg** & **Std** \\ \hline Data 6 & 68.89\% & 1.43\% & 69.48\% & 1.23\% & 68.37\% & 2.68\% & 67.78\% & 1.44\% & **77.80\%** & 3.11\% \\ Data 7 & 68.89\% & 1.43\% & 69.48\% & 1.23\% & 68.37\% & 2.68\% & 67.78\% & 1.44\% & **78.19\%** & 2.63\% \\ Data 8 & 69.25\% & 1.44\% & 68.99\% & 1.49\% & 68.24\% & 2.60\% & 69.29\% & 1.21\% & **77.18\%** & 3.08\% \\ Data 9 & 69.25\% & 1.44\% & 68.99\% & 1.49\% & 68.24\% & 2.60\% & 69.29\% & 1.21\% & **77.14\%** & 3.21\% \\ Data 10 & 68.53\% & 1.82\% & 69.78\% & 2.43\% & 70.17\% & 2.32\% & 69.02\% & 1.59\%A summary and visualized representation of the results can be observed by the radar chart of Figure 4 for both the accuracy and F-measure values for both first-rules and latter-rules datasets. It is clearly observed from the figures that the values of the accuracy and F-measure for the proposed WOA-SVM approach, which is represented by the red polygon, are much higher than the other values for the standard algorithms, having a red polygon surrounding all the other polygons. It is also observed that the proposed approach has even advanced values compared to the other algorithms for the F-measure. In addition, the values of the F-measure for the standard algorithms are clearly very low for the first-rules datasets, as the corresponding polygons are closer to the center of the radar compared to the other figures, which is represented by Figure 4c. \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{2-11}  & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** \\ \hline Data 1 & 51.48\% & 1.08\% & 45.53\% & 1.05\% & 40.40\% & 1.06\% & 30.63\% & 1.03\% & 79.03\% & 3.40\% \\ Data 2 & 51.48\% & 1.08\% & 45.53\% & 1.05\% & 40.40\% & 1.06\% & 30.63\% & 1.03\% & 80.36\% & 3.06\% \\ Data 3 & 47.74\% & 1.06\% & 40.68\% & 1.05\% & 40.40\% & 1.06\% & 32.30\% & 1.01\% & 81.12\% & 3.35\% \\ Data 4 & 47.74\% & 1.06\% & 40.66\% & 1.05\% & 40.40\% & 1.06\% & 32.30\% & 1.01\% & 81.96\% & 1.73\% \\ Data 5 & 53.91\% & 1.02\% & 41.87\% & 1.10\% & 40.68\% & 1.01\% & 31.17\% & 1.09\% & 80.66\% & 2.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: F-measure results of standard classifiers against WOA-SVM for First-rules datasets. \begin{table}
\begin{tabular}{c 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{2-11}  & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** \\ \hline Data 6 & 62.90\% & 0.30\% & 61.54\% & 0.26\% & 53.33\% & 1.09\% & 55.88\% & 0.44\% & 84.48\% & 3.00\% \\ Data 7 & 62.90\% & 0.30\% & 61.54\% & 0.26\% & 53.33\% & 1.09\% & 55.88\% & 0.44\% & 84.64\% & 2.16\% \\ Data 8 & 63.13\% & 0.26\% & 60.05\% & 0.30\% & 53.01\% & 1.10\% & 57.48\% & 0.44\% & 83.58\% & 2.46\% \\ Data 9 & 63.13\% & 0.26\% & 60.05\% & 0.30\% & 53.01\% & 1.10\% & 57.48\% & 0.44\% & 83.55\% & 2.32\% \\ Data 10 & 64.15\% & 0.39\% & 62.90\% & 0.53\% & 60.51\% & 0.48\% & 59.09\% & 0.41\% & 84.59\% & 2.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: F-measure results of standard classifiers against WOA-SVM for Latter-rules datasets. Figure 4: _Cont._  
Figure 4: Radar chart for comparing standard classification algorithms in terms of accuracy and F-measure.