Vehicle Classification using Machine Learning Techniques

During the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in vehicle classification. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model, best learning strategy, and the best feature selection method. Hence, four different vehicle datasets have been used to train seventeen classification models and five well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, Precision, and Recall. The results reveal that RandomForest and LMT are the best classifiers when it comes to handling vehicle datasets respectively. Considering the second objective, the Trees strategy showed the best performance.Furthermore,CorrelationAttributeEval,and ReliefFAttributeEval, are the best choices for handling the step of feature selection.