A robust SVM-based approach with feature selection and outliers detection for classification problems.

• A new model to classify data based on Support vector machines is introduced. • The model deals with outlier detection and feature selection simultaneously. • Strategies for initializing the big M parameters of the model are developed. • An heuristic algorithm based on kernel search to compute the classifier is presented. • The classification performance of the model is tested on real-life datasets. 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. [ABSTRACT FROM AUTHOR]