Learning the parameters of an outranking-based sorting model with characteristic class profiles from large sets of assignment examples

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We address the problem of learning the parameters of the outranking-based multiple criteria sorting model from large sets of assignment examples. We focus on a recently devised method called Electre TRI-rC, incorporating a single characteristic profile to describe each decision class. We introduce four algorithms aimed at the problem. They use different optimization techniques, including an evolutionary algorithm, linear programming combined with a genetic approach, simulated annealing, and a dedicated heuristic. We present the results of the experiments carried out on both artificial and real-world data sets. They reveal an impact of the comparison and veto thresholds, various sorting rules, and ensembles on the classification accuracy of the proposed algorithms. From a broader perspective, we contribute to cross-fertilizing the fields of Multiple Criteria Decision Aiding and Machine Learning for supporting real-world decision-making.