A new decision-making approach for multiple criteria sorting with an imbalanced set of assignment examples

We propose a novel approach to address a multiple criteria sorting (MCS) problem with an imbalanced set of assignment examples. The approach employs a piecewise-linear additive value function as the preference model and adopts the disaggregation–aggregation paradigm to infer a sorting model from provided assignment examples on a set of reference alternatives. We utilize a hierarchical clustering algorithm and several linear programming models to...