Fuzzy measure theory

Let ${\displaystyle \mathbf {X} }$ be a universe of discourse, ${\displaystyle {\mathcal {C}}}$ be a class of subsets of ${\displaystyle \mathbf {X} }$, and ${\displaystyle E,F\in {\mathcal {C}}}$. A function ${\displaystyle g:{\mathcal {C}}\to \mathbb {R} }$ where

1. ${\displaystyle \emptyset \in {\mathcal {C}}\Rightarrow g(\emptyset )=0}$
2. ${\displaystyle E\subseteq F\Rightarrow g(E)\leq g(F)}$

is called a fuzzy measure. A fuzzy measure is called normalized or regular if ${\displaystyle g(\mathbf {X} )=1}$.

A fuzzy measure is:

• additive if for any ${\displaystyle E,F\in {\mathcal {C}}}$ such that ${\displaystyle E\cap F=\emptyset }$, we have ${\displaystyle g(E\cup F)=g(E)+g(F).}$;
• supermodular if for any ${\displaystyle E,F\in {\mathcal {C}}}$, we have ${\displaystyle g(E\cup F)+g(E\cap F)\geq g(E)+g(F)}$;
• submodular if for any ${\displaystyle E,F\in {\mathcal {C}}}$, we have ${\displaystyle g(E\cup F)+g(E\cap F)\leq g(E)+g(F)}$;
• superadditive if for any ${\displaystyle E,F\in {\mathcal {C}}}$ such that ${\displaystyle E\cap F=\emptyset }$, we have ${\displaystyle g(E\cup F)\geq g(E)+g(F)}$;
• subadditive if for any ${\displaystyle E,F\in {\mathcal {C}}}$ such that ${\displaystyle E\cap F=\emptyset }$, we have ${\displaystyle g(E\cup F)\leq g(E)+g(F)}$;
• symmetric if for any ${\displaystyle E,F\in {\mathcal {C}}}$, we have ${\displaystyle |E|=|F|}$ implies ${\displaystyle g(E)=g(F)}$;
• Boolean if for any ${\displaystyle E\in {\mathcal {C}}}$, we have ${\displaystyle g(E)=0}$ or ${\displaystyle g(E)=1}$.

Understanding the properties of fuzzy measures is useful in application. When a fuzzy measure is used to define a function such as the Sugeno integral or Choquet integral, these properties will be crucial in understanding the function's behavior. For instance, the Choquet integral with respect to an additive fuzzy measure reduces to the Lebesgue integral. In discrete cases, a symmetric fuzzy measure will result in the ordered weighted averaging (OWA) operator. Submodular fuzzy measures result in convex functions, while supermodular fuzzy measures result in concave functions when used to define a Choquet integral.

Let g be a fuzzy measure, the Möbius representation of g is given by the set function M, where for every ${\displaystyle E,F\subseteq X}$,

${\displaystyle M(E)=\sum _{F\subseteq E}(-1)^{|E\backslash F|}g(F).}$

The equivalent axioms in Möbius representation are:

1. ${\displaystyle M(\emptyset )=0}$.
2. ${\displaystyle \sum _{F\subseteq E|i\in F}M(F)\geq 0}$, for all ${\displaystyle E\subseteq \mathbf {X} }$ and all ${\displaystyle i\in E}$

A fuzzy measure in Möbius representation M is called normalized if ${\displaystyle \sum _{E\subseteq \mathbf {X} }M(E)=1.}$

Möbius representation can be used to give an indication of which subsets of X interact with one another. For instance, an additive fuzzy measure has Möbius values all equal to zero except for singletons. The fuzzy measure g in standard representation can be recovered from the Möbius form using the Zeta transform:

${\displaystyle g(E)=\sum _{F\subseteq E}M(F),\forall E\subseteq \mathbf {X} .}$

Fuzzy measures are defined on a semiring of sets or monotone class which may be as granular as the power set of X, and even in discrete cases the number of variables can as large as 2^|X|. For this reason, in the context of multi-criteria decision analysis and other disciplines, simplification assumptions on the fuzzy measure have been introduced so that it is less computationally expensive to determine and use. For instance, when it is assumed the fuzzy measure is additive, it will hold that ${\displaystyle g(E)=\sum _{i\in E}g(\{i\})}$ and the values of the fuzzy measure can be evaluated from the values on X. Similarly, a symmetric fuzzy measure is defined uniquely by |X| values. Two important fuzzy measures that can be used are the Sugeno- or ${\displaystyle \lambda }$-fuzzy measure and k-additive measures, introduced by Sugeno[2] and Grabisch[3] respectively.

In game theory, the Shapley value or Shapley index is used to indicate the weight of a game. Shapley values can be calculated for fuzzy measures in order to give some indication of the importance of each singleton. In the case of additive fuzzy measures, the Shapley value will be the same as each singleton.

For a given fuzzy measure g, and ${\displaystyle |\mathbf {X} |=n}$, the Shapley index for every ${\displaystyle i,\dots ,n\in X}$ is:

${\displaystyle \phi (i)=\sum _{E\subseteq \mathbf {X} \backslash \{i\}}{\frac {(n-|E|-1)!|E|!}{n!}}[g(E\cup \{i\})-g(E)].}$

The Shapley value is the vector ${\displaystyle \mathbf {\phi } (g)=(\psi (1),\dots ,\psi (n)).}$

• Probability theory
• Possibility theory

• Beliakov, Pradera and Calvo, Aggregation Functions: A Guide for Practitioners, Springer, New York 2007.
• Wang, Zhenyuan, and, George J. Klir, Fuzzy Measure Theory, Plenum Press, New York, 1991.

• Fuzzy Measure Theory at Fuzzy Image Processing