###### Abstract
However, the correlation coefficient can cause relevant features to be discarded if the correlation of the non-linear association is too small, thereby providing incorrect feature ranking [14] which increases the chances of developing a model that will give a poor outcome. Similarly, the association of two random variables may not be uniformly strong enough which may lead to small values of correlation, resulting in the assumption that association between the variables does not exist [15]. Most of the traditional techniques for the selection of optimal feature subsets mostly experience enormous complexity, premature convergence and expensive computational cost. As a result, nature-inspired metaheuristic algorithms were proposed to mitigate the limitations of the traditional methods which are found to be efficient and effective in minimizing feature subsets and maximizes the model accuracy [3]. For example, the non-dominated sorted genetic algorithm III (NSGA3) has proven to outperform many feature selection algorithms such as the indicator-based evolutionary algorithm, NSGA2, the strength Pareto evolutionary algorithm II and the kernel-based nonlinear extension of Arps decline [16]. To deviate from the limitations of the correlation coefficient feature selection method typically used in the domain of IoV, this paper proposed a multi-objective, filter-based hybrid non-dominated sorted genetic algorithm III (NSGA3) with a gain ratio (GR) and a bi-directional wrapper for the selection of subset features influencing vehicle collision in the IoV to minimize features and maximize accuracy of the model. In a situation where the filter or wrapper cannot effectively produce the best subsets of features, a hybrid approach is applied. Hence, this study uses the benefits of the filter and wrapper to minimize the limitations of the filter and wrapper to improve classification performance with the best subsets of features. The paper is organized as follows. Section 2 briefly discusses the features influencing vehicle collision in the IoV and the basics of the IoV. Section 3 describes the basics operations of feature selection algorithms and the adopted algorithms. Section 4 presents the methodology, Section 5 discusses the results and finally, Section 6 concludes the paper. A summary of the research contributions is as follows:  
* A hybrid NSGA III Bi-directional gain ratio is proposed that combines wrapper and filter methods for selecting vehicle collision subset features in the IoV. * The study reveals the optimal subset features required for vehicle collision detection in the Internet of Vehicles. * The evaluation study shows that the performance of the hybrid, bi-directional NSGA III is better than the other compared algorithms. * IoV vehicle collision detection features are reduced to the minimal amount and the accuracy of collision detection in the IoV is maximized. * It is possible to develop a vehicle collision alert system for collision detection of vehicles in the IoV using only three subset features. * The proposed algorithm has the potential to be used to develop vehicle collision alarm systems with an improved performance that can assist drivers/self-driving cars to avoid vehicle collision faster and better.