Distributionally robust chance constrained svm model with $\ell_2$-Wasserstein distance

In this paper, we propose a distributionally robust chance-constrained SVM model with \begin{document}$ \ell_2 $\end{document} -Wasserstein ambiguity. We present equivalent formulations of distributionally robust chance constraints based on \begin{document}$ \ell_2 $\end{document} -Wasserstein ambiguity. In terms of this method, the distributionally robust chance-constrained SVM model can be transformed into a solvable linear 0-1 mixed integer programming problem when the \begin{document}$ \ell_2 $\end{document} -Wasserstein distance is discrete form. The DRCC-SVM model could be transformed into a tractable 0-1 mixed-integer SOCP programming problem for the continuous case. Finally, numerical experiments are given to illustrate the effectiveness and feasibility of our model.