  This paper proposes a robust classification model, based on support vector
machine (SVM), which simultaneously deals with outliers detection and feature
selection. The classifier is built considering the ramp loss margin error and
it includes a budget constraint to limit the number of selected features. The
search of this classifier is modeled using a mixed-integer formulation with big
M parameters. Two different approaches (exact and heuristic) are proposed to
solve the model. The heuristic approach is validated by comparing the quality
of the solutions provided by this approach with the exact approach. In
addition, the classifiers obtained with the heuristic method are tested and
compared with existing SVM-based models to demonstrate their efficiency.
