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