A Robust Nonlinear Support Vector Machine Approach for Vehicles Smog Rating Classification

Nowadays all new vehicles are labelled in terms of their emissions thanks to ad hoc legislation. However, from a practical perspective, it is difficult to rank all of them. This paper considers the problem of classifying vehicles in terms of smog rating emissions by adopting a Machine Learning technique. Specifically, a new Support Vector Machine approach is considered, designed for nonlinear separating decision boundaries. To protect the model against uncertainty arising in the measurement procedure, a robust optimization model with spherical uncertainty sets is formulated. Numerical results are performed on both synthetic and real-world datasets, showing the good performance of the proposed formulation.