We consider polyhedral separation of sets as a possible tool in supervised classification. In particular, we focus on the
optimization model introduced by Astorino and Gaudioso (J Optim Theory Appl 112(2):265–293, 2002) and adopt its
reformulation in difference of convex (DC) form. We tackle the problem by adapting the algorithm for DC programming
known as DCA. We present the results of the implementation of DCA on a number of benchmark classification datasets
