SMAA-Choquet-FlowSort: A novel user-preference-driven Choquet classifier applied to supplier evaluation

The Choquet integral has been used as an aggregation operator to deal with interacting criteria in different types of problems. For ordinal classification problems, the majority of Choquet integral-based models proposed in the literature are built upon a supervised machine learning perspective, where a training data set is considered. Despite the effectiveness of these classification algorithms, their training-data-dependency may be considered a drawback in some decision-making problems. Our goal is then to propose a new multiple criteria Choquet classifier to conduct sorting based on user preference information. The classifier is built inside the FlowSort framework and, as such, aggregates intensity of preferences with respect to pairs of criteria instead of directly aggregating the criteria evaluations. This characteristic allows criteria and limiting profiles to be assessed by heterogeneous scales. In addition, we apply the Stochastic Multi-criteria Acceptability Analysis to the proposed classifier in order to elicit the Choquet capacities, model uncertain input data and analyze robustness of the results. The proposed classifier is applied to a real decision problem regarding the evaluation of pharmaceutical suppliers.