Multi-dimensional data aggregation utilizing extended partitioned Bonferroni mean Operator

In this contribution, we develop the concept of an Extended Partitioned Bonferroni Mean \left({{\mathcal{E}}{\mathcal{P}}{\mathcal{B}}{\mathcal{M}}}\right) operator, which is efficient enough to aggregate input vectors with a varying number of components integrated with some dependence pattern. The...

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Bibliographic Details
Main Authors: Banerjee, Debasmita, Guha, Debashree, Mesiar, Radko
Format: Conference Proceeding
Language:English
Subjects:
Extended aggregation function
Global monotonicity
Partitioned Bon-ferroni mean
Online Access:Request full text
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Summary:In this contribution, we develop the concept of an Extended Partitioned Bonferroni Mean \left({{\mathcal{E}}{\mathcal{P}}{\mathcal{B}}{\mathcal{M}}}\right) 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 {\mathcal{E}}{\mathcal{P}}{\mathcal{B}}{\mathcal{M}} 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 {\mathcal{E}}{\mathcal{P}}{\mathcal{B}}{\mathcal{M}} operator employing the Max-Entropy technique.
ISSN:1558-4739
DOI:10.1109/FUZZ48607.2020.9177811