## 5 Results and Discussion
Table 2 indicates that the NSGA2 +GR with the five different classifiers shows steady results when three features are selected by NSGA2 + GR to build the SVM and the GNB classifiers, with the GNB recording the highest accuracy of 88.51 on the three selected IoV vehicle collision detection subset features. On the other hand, KNN recorded the highest accuracy in the category without feature selection compared to the SVM and the GNB. The four subsets of IoV vehicle collision detection features are consistent with the KNN, while five features are consistent with the RFC classifier. Comparing the performance of all classifiers, it can be observed that those with the highest accuracy selected only three features, followed by the classifier with four features. Even though RFC is stable on four features, it recorded a low accuracy. Hence, it can be deduced that GNB can produce better accuracy in detecting collision in the IoV with only three features selected by NSGA2 + GR from the available IoV vehicle collision features. It is observed that the feature selection results in all cases outperformed the classifiers developed with all the IoV vehicle collision detection features without feature selection. Figure 4 depicts the curve for the different classifiers with selected features based on NSGA2 + GR. Similarly, Table 2 indicates that the results of the NSGA3 + GR feature selection approach show that the SVM outperforms the KNN and the GNB both in terms of the number of selected features and accuracy. Thus, comparing the performances of both NSGA2 + GA and NSGA3 + GA, it is found that the NSGA3 + GA with SVM performs better in terms of both the number of IoV vehicle collision selected features, as well as collision detection accuracy. As observed in the case of NSGA2 + GA, similarly, NSGA3 + GA shows better performance with selected IoV vehicle collision detection features compared to the result for all the features without selection. Figure 5 shows the performance of each classifier. NSGA3 + GR with SVM recorded the highest detection accuracy and was stable on three subsets of features. NSGA3 + GR with KNN, GNB and RF is stable on four subset features, with RFC attaining the highest accuracy among them. On the other hand, DTC is  
Figure 10: Performance comparison for NSGA2 and NSGA3 across filter-, wrapper- and hybrid-based feature selection methods. stable on five features, but recorded the lowest accuracy apart from the KNN. Comparing the performance of all the five classifiers in terms of accuracy clearly shows that SVM outperforms the compared classifiers and selects the least number of features. The results of wrapper-based NSGA2 + Bi-directional indicated that it is steady when three features are selected by the wrapper-based NSGA2 + Bi-directional and it achieved the best performance with the SVM classifier compared to the other classifiers. The wrapper-based NSGA2 + Bi-directional recorded 91.44 as the best accuracy with three subset features. With all the features, the highest accuracy without feature selection was recorded by wrapper-based NSGA2 + Bi-directional with the SVM classifier, showing that feature selection by NSGA2 + Bi-directional has improved the accuracy in detecting vehicle collision in the IoV. Figure 6 presents the learning curve for the different classifiers with subset features selected by wrapper-based NSGA2 + Bi-directional. Figure 7 presents the performance of the classifiers developed based on wrapper-based NSGA3 + Bi-directional. The wrapper-based NSGA3 + Bi-directional with the SVM has the highest vehicle collision detection accuracy in the IoV and it is stable on three feature subsets. The wrapper-based NSGA3 + Bi-directional with the DT, GNB and RF is stable on three subsets of the features, whereas the KNN is stable on four features. Comparing the performance of all the five classifiers in terms of accuracy clearly shows that the SVM outperforms all the other classifiers with 98.97% vehicle collision detection accuracy in the IoV.