Lecture Notes in Electrical Engineering.
Springer Verlag.
Vol. 730.
2022.
P. 593-604
On Random Generation of Supermodular Capacities
Discrete fuzzy measures (capacities) are a powerful tool to model interacting inputs and aggregate them using Choquet, Sugeno, and other fuzzy integrals. Simulation studies and probabilistic optimization algorithms require efficient random sampling from the set of fuzzy measures. It is a challenging problem due to an extremely high complexity of the polytope of capacities. This article addresses random sampling from two special classes: supermodular and submodular fuzzy measures. A suitable marginal contributions representation converts the problem to sampling from an order polytope, which is dealt efficiently using the Markov chain random walk. © 1993-2012 IEEE.
Authors
Beliakov G.
1,
2
Publisher
Institute of Electrical and Electronics Engineers Inc.
Number of issue
1
Language
English
Pages
293-296
Status
Published
Link
Volume
30
Year
2022
Organizations
- 1 School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
- 2 University of Russia (RUDN University), Moscow, 117198, Russian Federation
Keywords
Capacity; fuzzy measure; multiple criteria decision making; random sampling; supermodularity
Date of creation
06.07.2022
Date of change
06.07.2022
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