Redefining support vector machines with the ordered weighted average

In this work, the classical soft-margin Support Vector Machine (SVM) formulation is redefined with the inclusion of an Ordered Weighted Averaging (OWA) operator. In particular, the hinge loss function is rewritten as a weighted sum of the slack variables to guarantee adequate model fit. The proposed two-step approach trains a soft-margin SVM first to obtain the slack variables, which are then used to induce the order for the OWA operator in a...