## 4 Methodology
### Proposed Multi-Objective Hybrid NSGA3  
The experiment platforms were as follows. Hardware platform: Mac M2 chip with an 8-core CPU having core performance and four efficiency cores, 10 cores, a GPU 16-core Neural engine and 100 GB/s bandwidth of memory. Software platform: The experiment was conducted in Anaconda Python platform. Parameter settings in Table 1 were adopted from [PERSON] and [PERSON] [32,33]. The parameter settings were used for executing the NSGA3 and NSGA2. This study used multi-objective feature selection that evaluates a group of features concurrently. The NSGA3, along with the GR as the filter evaluation measure and bi-directional, is used for the selection of IoV collision features with the most significant informative subset features. For the purpose of comparison within the family of NSGA, NSGA2 with the GR and bi-directional is used across filter-, wrapper- and hybrid-based feature selection methods for the selection of features influencing vehicle collision in the IoV. In the procedure for executing the multi-objective NSGA3 for the feature selection, binary chromosomes were adopted for the selection of the IoV features influencing vehicle collision within the IoV environment. Because the proposed study is multi-objective, the study aims to minimize the IoV features influencing vehicle collision and maximize the accuracy of vehicle collision detection in the IoV. Each group of the subset features is \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Description** & **Settings** \\ \hline Reference points number & 0.9 \\ Crossover probability & 0.9 \\ Crossover distribution index & 0.25 \\ Mutation probability & 0.2 \\ Hypervolume & [0, 0]–[1, 1] \\ Generations & 500 \\ Population size & 200 \\ Population initialization & Random \\ \hline Objectives & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: NSGA family parameter setting. Figure 2: Some of the features influencing vehicle collision in the IoV environment. selected by the NSGA2 and NSGA3. The filter-based method with GR is used to rank all the subsets of features along with the classification performance for both NSGA3 and NSGA2. The features are stored in order of their classification performance. Classifiers are used for the evaluation of the discriminant value of the IoV feature subset selected by the NSGA2 and NSGA3. The study used support vector machine (SVM), random forest (RFC), Gaussian naive Bayes (GNB), decision tree (DTC), and K-nearest neighbor (KNN) as the learning algorithms for developing the classifier. The 5 algorithms were chosen because the algorithms continue to be relevant in the literature, especially in the situation where data are not in large-scale volume. The IoV feature subset selected was used to develop 5 different classifiers for the purpose of evaluating the performance of the feature subset the chromosomes were able to identify. The 5 classifiers were subjected to detect vehicle collision in the IoV based on the selected optimum feature subset. The vehicle collision detection accuracy to be maximized is returned by each of the classifiers with a different combination of the feature subset. The experiment is repeated with the wrapper method that uses the bi-directional concept. The bi-directional operation is comparable to the forward way of selection with a distinction of evaluating the significance of any existing features before adding another new subset feature. If vehicle collision features in the data are deemed to be insignificant, the feature is quickly eliminated by backward selection. As a result, it combines backward elimination with forward selection. The stages involved in the bi-directional removal of features includes entering and leaving the model, selection of a significance level, carrying out subsequent forward selection, then complete removal of the feature in backward operation. The optimal subset of features is obtained by repeating the procedure as necessary.