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.

中文翻译：

---

通过凸（DC）编程差异进行多面体分离

我们认为集合的多面体分离是监督分类中的一种可能工具。特别是，我们关注于Astorino和Gaudioso引入的优化模型（J Optim Theory Appl 112（2）：265-293，2002），并采用凸（DC）形式的差异进行了重构。我们通过调整DC编程算法DCA来解决该问题。我们介绍了在许多基准分类数据集上实施DCA的结果。