A novel multicriteria decision aiding method based on unsupervised aggregation via the Choquet integral

In multicriteria decision aiding (MCDA), the Choquet integral has been used as an aggregation operator to deal with the case of interacting decision criteria. In this context, a practical problem that arises is related to the identification of the parameters associated with the Choquet integral, which are known as the Choquet capacities. In this paper, we address the problem of capacity identification by means of unsupervised learning, which, in MCDA, refers to the situations in which only the decision matrix is available. Our contribution is twofold. First, we discuss the extension of some previous works on the subject as well as some of their limitations. Then, we introduce a novel method, which is able to associate the parameters of the Choquet integral with the decision table correlation structure. As attested by numerical experiments, the proposed approach is conceptually simple to be implemented and can detect interactions between criteria in a data-driven fashion.