###### Abstract  
The smart platform of generating, collecting, managing and processing dynamic data from different sources in the Internet of Vehicles (IoV) pave the way for a large-scale dataset to be accumulated. The dataset can contain records running into hundreds of thousands and even millions of relevant, irrelevant and redundant features. Therefore, feature selection to select only the significant features for developing vehicle collision detection alarm systems for deployment in the IoV edge is critical. However, previous studies on vehicle collision detection in the IoV have not conducted rigorous feature selection. Limited studies have mainly applied Pearson correlation coefficient (PCC) to select subset features influencing vehicle collision in the domain of IoV. However, PCC can cause relevant features to be discarded if the correlation of the non-linear association is too small, thereby providing incorrect feature ranking, which, in turn, increases the chances of developing a model that will give a poor outcome. To close this gap, this paper proposed a multi-objective, filter-based hybrid non-dominated sorted genetic algorithm III with a gain ratio and bi-directional wrapper for the selection of subset features influencing vehicle collision in the IoV. The proposed approach selected the minimal most significant subset features for developing a vehicle collision detection classifier with maximum accuracy for deployment in the IoV. A comparative study proves that the proposed approach performs better than the compared algorithms across hybrid-, wrapper- and filter-based feature selection methods within the family of the NSGA. Further, a comparative analysis with other evolutionary algorithms proves the superiority of the proposal. This study can help researchers in the future by avoiding the use of large-scale computing resources in acquiring data to develop vehicle collision alert systems in the IoV. This can be achieved since only the subset features discovered in this study are collected, as opposed to collecting large features, thus saving time and resources in the subsequent vehicle collision detection data collection in the IoV. feature selection; filter; gain ratio; Internet of Vehicles; multi-objective NSGA III; vehicle collision detection; sensors; wrapper +
Footnote †: journal: _Applied Sciences_  
that the information expected to be generated by the IoV will be greater than that of the telecommunications industries. Data generation in the real-world environment is mostly performed in a very large scale which makes the management of the data very complex. Such data typically comprises large numbers of features. In reality, it is not always true that all the features generated from the data are significant for obtaining useful information. Irrelevant and redundant features with the tendency to reduce the algorithm performance can be contained in the features. As such, removing the redundant and irrelevant features while maintaining the performance accuracy of the algorithm is the major aim of conducting feature selection [3]. The availability of large-scale data with hundreds of features has prompted the research community to propose different feature selection methods. Feature selection has the advantage of removing noisy, irrelevant or redundant data; it reduces the convergence time of the algorithm; it improves the quality of the data and the model accuracy can also be improved; and it can save resources in subsequent data collection [4]. Therefore, the selection features should contain only the relevant information required to solve a particular problem [5]. One of the problems of vehicular mobility in the IoV is the collision of vehicles that can lead to injury, permanent disability, vehicle damage, traffic congestion, and the loss of lives and property. Detecting the possibility of vehicle collision in the IoV ahead of time can help the driver of a conventional vehicle or a self-driving vehicle to avoid collision.