## 4 Results
### Comparison of Optimal Feature Model with Baseline Model  
In the last two subsections we show how the baseline model and machine learning models with optimal feature sets were modelled. In this subsection, the authors quantitatively analyse the improvements in performance of those machine learning models generated by the optimal feature sets compared to the baseline model [28; 29]. The visual differences in accuracy and F1-score can be seen in the bar graph in Figures 7 and 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**F1-Score Using**} & \multicolumn{3}{c}{**F1-Score Using**} & \multicolumn{3}{c}{**F1-Score Using**} \\  & \multicolumn{3}{c}{**60 Features**} & \multicolumn{3}{c}{**30 Features**} & \multicolumn{3}{c}{**20 Features**} \\ \cline{2-11}  & **RF** & **GB** & **AB** & **RF** & **GB** & **AB** & **RF** & **GB** & **AB** \\ \hline LR & 0.9703 & 0.9700 & 0.9699 & 0.9675 & 0.9667 & 0.9675 & 0.9406 & 0.9552 & 0.9651 \\ \hline SVM & 0.9885 & 0.9885 & **0.9886** & **0.9908** & 0.9905 & 0.9893 & 0.9843 & **0.9876** & 0.9857 \\ \hline NB & 0.8645 & 0.8747 & 0.8767 & 0.9338 & 0.9454 & 0.9172 & 0.9304 & 0.9434 & 0.9189 \\ \hline \hline \end{tabular}
\end{table}
Table 4: F1-Score of 27 models (proposed) using various feature set and feature selection techniques.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**No. of Features**} & \multirow{2}{*}{**Random Forest**} & \multirow{2}{*}{**Gradient Boost**} & \multirow{2}{*}{**Ada Boost**} \\
**Selected** & & & & \\ \hline
60 & 21.009 s & 133.8944 s & 158.044 s \\
30 & 30.4625 s & 183.720 s & 222.788 s \\
20 & 36.819 s & 190.019 s & 234.9901 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time taken by ensemble feature selection algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**\% Accuracy Using**} & \multicolumn{3}{c}{**\% Accuracy Using**} & \multicolumn{3}{c}{**\% Accuracy Using**} \\  & \multicolumn{3}{c}{**60 Features**} & \multicolumn{3}{c}{**30 Features**} & \multicolumn{3}{c}{**20 Features**} \\ \cline{2-11}  & **RF** & **GB** & **AB** & **RF** & **GB** & **AB** & **RF** & **GB** & **AB** \\ \hline LR & 97.04 & 97.02 & 97.01 & 96.77 & 96.69 & 96.77 & 94.09 & 95.58 & 96.53 \\ \hline SVM & 98.86 & 98.86 & **98.87** & **99.08** & 99.06 & 98.94 & 98.43 & **98.77** & 98.58 \\ \hline NB & 86.96 & 87.90 & 88.09 & 93.39 & 94.58 & 91.73 & 93.06 & 94.39 & 91.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of 27 models (proposed) using various feature sets and feature selection techniques.  
Figure 7: Accuracy comparison of optimal feature set model and baseline model.  
Figure 7 shows the highest accuracy achieved by the combination of various feature selection and machine learning techniques for a varying feature set containing 60, 30 or 20 features. It also shows that each of our optimal feature models with a lesser number of features compared to our baseline model outperformed the performance of our baseline model, which had an accuracy of 87.206%.  
Similar inferences can be made from Figure 8, which shows the highest F1-score achieved by the combination of various feature selection and machine learning techniques for a varying feature set containing 60, 30 or 20 features