Abstract
This paper considers the issue of how to include large positive and negative differences in multicriteria sorting models, based on the outranking paradigm. We show that various outranking relations can be constructed to take into account these out- and under-performances, before presenting mathematical programs to learn the preferential parameters of these models.
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