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.

Polyhedral separation via difference of convex (DC) programming

Francesco, Massimo Di;Gaudioso, Manlio;Gorgone, Enrico;Manca, Benedetto
2021

Abstract

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.
classification; machine learning; DC optimization
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/318544
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