Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria

We propose a new approach to preference model learning for multiple criteria sorting within the regularization framework traditionally used in the statistical learning theory. It employs an additive piecewise-linear value function as a preference model, and infers the model’s parameters from the assignment examples concerning a subset of reference alternatives. As such, our approach belongs to the family of preference disaggregation approaches....