Generating fuzzy measures from additive measures
Abstract
Fuzzy measures, also referred to as nonadditive measures, emerge from the foundational concept of additive measures by transforming additivity into monotonicity. In comparison to the expansive coefficients of fuzzy measures, additive measures encompass just coefficients, enabling them to efficiently provide initial viable solutions across various domains including normal, super/submodular, super/subadditive, (anti)buoyant, and other specialized fuzzy measures. To further enhance the effectiveness of these measures, techniques such as allowable range adjustments and random walks have been introduced, aiming to decrease the redundancy in linear extensions and bolster the random or uniform nature of the generated measures. In addition to innovating the ideas of random generation and adjustment strategies for multiple types of fuzzy measures, this paper also sheds light on the profound connection between additive and fuzzy measures.
Cite
@article{arxiv.2309.15399,
title = {Generating fuzzy measures from additive measures},
author = {Jian-Zhang Wu and Gleb Beliakov},
journal= {arXiv preprint arXiv:2309.15399},
year = {2023}
}