## 5 Results and Discussion  
The default settings of two multi-objective feature selections (NSGA3 and NSGA2) along with filter-based GR were run on the datasets. The performance of the five different classifiers are recorded as shown in Table 2. Table 2 can infer the most relevant number of features with better accuracy. From the graphs plotted in Figures 4-10, the \(x\)-axis represents the number of IoV vehicle collision detection selected features, whereas the \(y\)-axis represents the classification accuracy recorded by each classifier on the forty independent runs for each of the algorithms. Figure 3: Propose framework for subset selection: (**A**) NSGA3 + GR, (**B**) NSGA3 + Bi-directional and (**C**) the NSGA3 + Bi-directional + GR. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**KNN**} & \multicolumn{2}{c}{**GNB**} & \multicolumn{2}{c}{**RFC**} & \multicolumn{2}{c}{**DTC**} \\ \cline{2-10}  & **Selected** & **Accuracy** & **Selected** & **Accuracy** & **Selected** & **Accuracy** & **Selected** & **Accuracy** & **Selected** & **Accuracy** \\  & **Features** & **Features** & **Features** & **Features** & **Features** & **Features** & **Features** & **Features** & **Features** \\ \hline \multirow{2}{*}{NSGA2 + CR} & All features (6) & 75.11 & All features & 81.24 & All features & 74.55 & All features & 80.5 & All features & 79.5 \\ \cline{2-10}  & F1 (3) & 88.22 & F3 (4) & 88.00 & F1 (3) & 88.51 & F4 (q) & 81.00 & F3 (q) & 81.5 \\ \cline{2-10}  & All features (6) & 89.64 & All features (6) & 84.92 & All features (6) & 85.00 & All features & 90.25 & All features & 89.0 \\ \cline{2-10}  & F1 (3) & **98.22** & F3 (4) & 90.34 & F4 (q) & 91.28 & F3 (q) & 92.75 & F5 (q) & 90.5 \\ \hline NSGA2 + & All features (6) & 90.2 & All features & 89.75 & All features & 89.55 & All features & 85.65 & All features & 82.50 \\ \cline{2-10} Bi-directional & F1 (3) & **91.44** & F2: (3) & 90.00 & F1 (3) & 90.00 & F1 (3) & 89.00 & F6 (2) & 89.50 \\ \hline NSGA3 + & All features (6) & 93.25 & All features (6) & 91.05 & All features (6) & 91.75 & All features & 92.25 & All features & 90.65 \\ \cline{2-10} Bi-directional & F1 (3) & **98.97** & F3 (4) & 91.99 & F1 (3) & 92.25 & F2 (3) & 93.55 & F2 (3) & 91.30 \\ \hline NSGA2 + & All features (6) & 92.55 & All features & 90.75 & All features & 91.05 & All features & 89.12 & All features & 89.77 \\ \cline{2-10} Bi-directional + CR & F1 (3) & **93.00** & F3 (4) & 91.75 & F1 (3) & 91.75 & F1 (3) & 90.05 & F3 (q) & 90.50 \\ \hline NSGA3 + & All features (6) & 99.25 & All features (6) & 92.05 & All features (6) & 93.15 & All features & 94.25 & All features & 90.65 \\ \cline{2-10} Bi-directional + CR & F1 (3) & **99.97** & F2: (3) & 92.24 & F1 (3) & 93.30 & F2 (3) & 94.58 & F2 (3) & 92.32 \\ \hline \hline \end{tabular}  
* F1: Distance between vehicles, velocity of the vehicle and breaking capability. F2, breaking capability, velocity of vehicle and number of lanes. F3: Number of lanes, velocity of vehicles, distance between vehicles and breaking capability. F6: Distance between vehicles, velocity of vehicle, breaking capability and nature of environment. F5: Number of environment, number of lanes, velocity of vehicle, distance between vehicles and breaking capability. F6: Breaking capability and distance between vehicles.  
\end{table} 
Table 2: Results of the independent runs of the algorithms for IoV vehicle collision feature selection and accuracy. Figure 4: NSGA2 + GR with different classifiers builds using subsets of the IoV vehicle collision detection features. Figure 5: NSGA3 + GR with various classifiers. Figure 6: Performance of the five classifiers developed with subset features selected by NSGA2 + Bi-directional. Figure 7: Wrapper-based NSGA3 + Bi-directional elimination for the classifiers. Figure 8: Hybrid NSGA2 + Bi-directional + GR feature selection performance. Figure 9: Hybrid NSGA3 + Bi-directional + GR feature selection performance.