Representation, optimization and generation of fuzzy measures

• DOI: 10.1016/j.inffus.2024.102295
• Publication Date: 2024-02-07T02:00:55Z
• Authors: Gleb Beliakov; Jian-Zhang Wu; Weiping Ding
• Keywords: Representation; Feature (linguistics)

We review recent literature on three aspects of fuzzy measures: their representations, learning optimal measures and random generation various types measures. These are interdependent: methods depend representation, may also include as one the steps, other hand different representations affect methods, while plays an important role in simulation studies for post-hoc analysis sets learned from data problem-specific constraints. Explicit modelling interactions between decision variables is a distinctive feature integrals based measures, but its price high computational complexity. To extend range applicability efficient techniques required. All mentioned provide mathematical tools novel applications making information fusion, allow scaling up significantly domain reduce