  We consider preference disaggregation in the context of multiple criteria
sorting. The value function parameters and thresholds separating the classes
are inferred from the Decision Maker's (DM's) assignment examples. Given the
multiplicity of sorting models compatible with indirect preferences, selecting
a single, representative one can be conducted differently. We review several
procedures for this purpose, aiming to identify the most discriminant, average,
central, benevolent, aggressive, parsimonious, or robust models. Also, we
present three novel procedures that implement the robust assignment rule in
practice. They exploit stochastic acceptabilities and maximize the support
given to the resulting assignments by all feasible sorting models. The
performance of sixteen procedures is verified on problem instances with
different complexities. The results of an experimental study indicate the most
efficient procedure in terms of classification accuracy, reproducing the DM's
model, and delivering the most robust assignments. These include approaches
identifying differently interpreted centers of the feasible polyhedron and
robust methods introduced in this paper. Moreover, we discuss how the
performance of all procedures is affected by different numbers of classes,
criteria, characteristic points, and reference assignments. Finally, we
illustrate the use of all approaches in a study concerning the assessment of
the green performance of European cities.
