  In this work we study binary classification problems where we assume that our
training data is subject to uncertainty, i.e. the precise data points are not
known. To tackle this issue in the field of robust machine learning the aim is
to develop models which are robust against small perturbations in the training
data. We study robust support vector machines (SVM) and extend the classical
approach by an ensemble method which iteratively solves a non-robust SVM on
different perturbations of the dataset, where the perturbations are derived by
an adversarial problem. Afterwards for classification of an unknown data point
we perform a majority vote of all calculated SVM solutions. We study three
different variants for the adversarial problem, the exact problem, a relaxed
variant and an efficient heuristic variant. While the exact and the relaxed
variant can be modeled using integer programming formulations, the heuristic
one can be implemented by an easy and efficient algorithm. All derived methods
are tested on random and realistic datasets and the results indicate that the
derived ensemble methods have a much more stable behaviour when changing the
protection level compared to the classical robust SVM model.
