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In many ranking problems, some particular aspects of the addressed situationshould be taken into account in the aggregation process. An example is thepresence of correlations between criteria, which may introduce bias in thederived ranking. In these cases, aggregation functions based on a capacity maybe used to overcome this inconvenience, such as the Choquet integral or themultilinear model. The adoption of such strategies requires a stage to estimatethe parameters of these aggregation operators. This task may be difficult insituations in which we do not have either further information about theseparameters or preferences given by the decision maker. Therefore, the aim ofthis paper is to deal with such situations through an unsupervised approach forcapacity identification based on the multilinear model. Our goal is to estimatea capacity that can mitigate the bias introduced by correlations in thedecision data and, therefore, to provide a fairer result. The viability of ourproposal is attested by numerical experiments with synthetic data

An unsupervised capacity identification approach based on Sobol' indices