## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Feature Selection** & \multirow{2}{*}{**Kernel Type**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**ROC**} & \multirow{2}{*}{**RMSE**} & \multicolumn{4}{c}{**F-Measure**} \\ \cline{6-10}
**Method** & & & & & & & **Low** & **Medium** & **High** & **Weighted Average** \\ \hline \multirow{3}{*}{Information Gain} & PolyKernel & 61.4 & 0.61 & 1 & 0.50 & 0.46 & 0.19 & 0.86 & 0.35 & 0.75 \\  & **Normalized** & **62.7** & **0.62** & **1.00** & **0.50** & **0.45** & **0.23** & **0.88** & **0.32** & **0.77** \\  & RBF Kernel & 62.0 & 0.62 & 1.00 & 0.50 & 0.46 & 0.21 & 0.87 & 0.35 & 0.77 \\ \hline \multirow{3}{*}{Wrapper Method} & PolyKernel & 60.9 & 0.61 & 1.00 & 0.50 & 0.46 & 0.20 & 0.84 & 0.37 & 0.75 \\  & Normalized & 58.9 & 0.59 & 1.00 & 0.50 & 0.47 & 0.18 & 0.86 & 0.35 & 0.74 \\  & PolyKernel & **63.6** & **0.63** & **1.00** & **0.50** & **0.45** & **0.22** & **0.87** & **0.33** & **0.77** \\ \hline \multirow{3}{*}{Relief-F} & PolyKernel & **68.5** & **0.68** & **1.00** & **0.48** & **0.41** & **0.25** & **0.92** & **0.39** & **0.81** \\  & Normalized & 67.8 & 0.68 & 0.99 & 0.49 & 0.42 & 0.27 & 0.90 & 0.34 & 0.80 \\  & PolyKernel & **68.5** & **0.68** & **1.00** & **0.48** & **0.41** & **0.21** & **0.90** & **0.42** & **0.81** \\ \hline \multirow{3}{*}{LASSO} & PolyKernel & 53.9 & 0.53 & 0.98 & 0.47 & 0.43 & 0.19 & 0.75 & 0.29 & 0.65 \\  & **Normalized** & **56.5** & **0.56** & **1.00** & **0.50** & **0.45** & **0.23** & **0.81** & **0.34** & **0.71** \\  & PolyKernel & 55.2 & 0.55 & 0.96 & 0.49 & 0.44 & 0.20 & 0.68 & 0.31 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of SVM classifier through various feature selection methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Feature Selection** & **Number of** & **Random Seed** & **Accuracy (\%)** & **Precision** & **Recall (\%)** & **ROC** & **RMSE** & & & & **F-Measure** \\ \cline{3-11}
**Method** & **Trees** & & & & & & & & & **Low** & **Medium** & **High** & **Weighted Average** \\ \hline  & **50** & **7** & **68.5** & **0.60** & **0.68** & **0.64** & **0.39** & **0.21** & **0.83** & **0.16** & **0.62** \\ Information Gain & 100 & 8 & 67.1 & 0.56 & 0.67 & 0.66 & 0.39 & 0.23 & 0.78 & 0.18 & 0.59 \\  & 100 & 11 & 66.4 & 0.57 & 0.66 & 0.65 & 0.39 & 0.15 & 0.78 & 0.21 & 0.59 \\  & **50** & **7** & **65.9** & **0.56** & **0.65** & **0.66** & **0.41** & **0.24** & **0.75** & **0.19** & **0.57** \\ Wrapper Method & 100 & 8 & 63.6 & 0.50 & 0.63 & 0.66 & 0.42 & 0.16 & 0.71 & 0.22 & 0.55 \\  & 100 & 11 & 63.6 & 0.49 & 0.63 & 0.65 & 0.41 & 0.21 & 0.69 & 0.17 & 0.54 \\  & 50 & 7 & 70.6 & 0.64 & 0.70 & 0.63 & 0.39 & 0.18 & 0.84 & 0.21 & 0.63 \\ Relief-F & **100** & **8** & **71.3** & **0.65** & **0.71** & **0.65** & **0.39** & **0.25** & **0.83** & **0.16** & **0.64** \\  & 100 & 10 & 69.2 & 0.60 & 0.69 & 0.64 & 0.39 & 0.17 & 0.83 & 0.14 & 0.62 \\  & 50 & 7 & 53.9 & 0.44 & 0.53 & 0.51 & 0.41 & 0.23 & 0.64 & 0.19 & 0.45 \\ LASSO & 100 & 8 & 55.4 & 0.47 & 0.55 & 0.53 & 0.44 & 0.26 & 0.65 & 0.17 & 0.48 \\  & **100** & **10** & **57.5** & **0.49** & **0.57** & **0.49** & **0.46** & **0.24** & **0.68** & **0.23** & **0.50** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of random forest classifier through various feature selection techniques. In contrast, the information gain-based feature selection method was employed. The model was again tuned with different combinations of values, and this process was repeated multiple times until the highest accuracy value was achieved. The highest accuracy of 73.2% was obtained when the ensemble size was set to 15 while using the relief-F based feature selection method. Complete results for the rotation forest classifier with various feature selection methods are presented in Table 7.  
After J48, SVM, random forest, and rotation forest, artificial neural networks were used to achieve more efficient results.