A generalization of the Bonferroni mean based on partitions

Beliakov, Gleb, James, Simon and Radko, Mesiar 2013, A generalization of the Bonferroni mean based on partitions, in FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE Computational Intelligence Society, Piscataway, N.J..

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Title A generalization of the Bonferroni mean based on partitions
Author(s) Beliakov, Gleb
James, Simon
Radko, Mesiar
Conference name IEEE International Conference on Fuzzy Systems (2013 : Hyderabad, India)
Conference location Hyderabad, India
Conference dates 7-10 Jul. 2013
Title of proceedings FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE International Conference on Fuzzy Systems
Total pages 6
Publisher IEEE Computational Intelligence Society
Place of publication Piscataway, N.J.
Keyword(s) aggregation functions
bonferroni mean
decision making
mandatory criteria
Summary The mean defined by Bonferroni in 1950 (known by the same name) averages all non-identical product pairs of the inputs. Its generalizations to date have been able to capture unique behavior that may be desired in some decision-making contexts such as the ability to model mandatory requirements. In this paper, we propose a composition that averages conjunctions between the respective means of a designated subset-size partition. We investigate the behavior of such a function and note the relationship within a given family as the subset size is changed. We found that the proposed function is able to more intuitively handle multiple mandatory requirements or mandatory input sets.
ISBN 9781479900220
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30060732

Document type: Conference Paper
Collection: School of Information Technology
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