Fuzzy measure theory

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Fuzzy measure theory considers a number of special classes of measures, each of which is characterized by a special property. Some of the measures used in this theory are plausibility and belief measures, fuzzy set membership function and the classical probability measures. In the fuzzy measure theory, the conditions are precise, but the information about an element alone is insufficient to determine which special classes of measure should be used. The central concept of fuzzy measure theory is fuzzy measure, which was introduced by Sugeno in 1974.

## Contents

* 1 Axioms
* 2 Examples of Fuzzy Measures
* 2.1 Sugeno <math>\lambda</math>-measure
* 2.1.1 Definition
* 3 See also
* 4 External links
* 5 References

## Axioms

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Fuzzy measure can be considered as generalization of the classical probability measure. A fuzzy measure g over a set X (the universe of discourse with the subsets E, F, ...) satisfies the following conditions:

1\. When E is the empty set then <math>g(E)=0</math>.

2\. When E is a subset of F, then <math>g(E)\leq g(F)</math>.

A fuzzy measure g is called normalized if <math>g(X)=1</math>.

## Examples of Fuzzy Measures

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### Sugeno <math>\lambda</math>-measure

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The Sugeno <math>\lambda</math>-measure is a special case of fuzzy measures defined iteratively. It has the following definition

#### Definition

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Let <math>X = \left\lbrace x_1,...,x_n \right\rbrace </math> be a finite set and let <math>\lambda \in (-1,+\infty)</math>. A Sugeno <math>\lambda</math>-measure is a function g from <math>2^X</math> to [0, 1] with properties:

1. <math>g(X) = 1</math>.
2. if A, B <math>\subseteq 2^X</math> with <math>A \cap B = \emptyset</math> then <math>g(A \cup B) =g(A)+g(B)+\lambda g(A)g(B)</math>.

As a convention, the value of g at a singleton set <math>\left\lbrace x_i \right\rbrace </math> is called a density and is denoted by <math>g_i = g(\left\lbrace x_i \right\rbrace)</math>. In addition, we have that <math>\lambda</math> satisﬁes the property

<math> \lambda +1 = \prod_{i=1}^n (1+\lambda g_i) </math>.

Tahani and Keller [1]as well as Wang and Klir have showed that that once the densities are known, it is possible to use the previous polynomial to obtain the values of <math>\lambda</math> uniquely.

## See also

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* Probability theory
* Possibility theory

## External links

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* http://pami.uwaterloo.ca/tizhoosh/measure.htm

## References

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* Wang, Zhenyuan, and , George J. Klir, Fuzzy Measure Theory, Plenum Press, New York, 1991.

1. ↑ H. Tahani and J. Keller (1990). "Information Fusion in Computer Vision Using the Fuzzy Integral". IEEE Transactions on Systems, Man and Cybernetic. 20 (3): 733–741.

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