Multi-dimensional data aggregation utilizing extended partitioned Bonferroni mean Operator

In this contribution, we develop the concept of an Extended Partitioned Bonferroni Mean (εPBM) operator, which is efficient enough to aggregate input vectors with a varying number of components integrated with some dependence pattern. The global monotonicity for the εPBM is analyzed by defining a new partition for each arity. Further to illustrate the applicability and feasibility of the proposed extended aggregation operator, an example based on medical device selection is demonstrated. Finally, we present a way to obtain the weights associated with the corresponding εPBM operator employing the Max-Entropy technique.