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

摘要: Abstract We propose a novel approach to address multiple criteria sorting (MCS) problem with an imbalanced set of assignment examples. The employs piecewise-linear additive value function as the preference model and adopts disaggregation–aggregation paradigm infer from provided examples on reference alternatives. utilize hierarchical clustering algorithm several linear programming models identify alternatives that are active develop model, so inactive ones eliminated whole Then, in order construct balanced examples, balancing is proposed balance across categories. Finally, obtained by minimizing sum violations between values corresponding category thresholds. Furthermore, performance investigated hypothetical real data sets. experimental results show our efficient MCS