Ordered Weighted Averaging (OWA) Aggregation Operators


Ordered Weighted Averaging (OWA) Aggregation Operators

Introduced by Ronald R. Yager, the Ordered Weighted Averaging operators, commonly called OWA operators, provide a parameterized class of mean type aggregation operators. Many notable mean operators such as the max, arithmetic average, median and min, are members of this class. They have been widely used in computational intelligence because of their ability to model linguistically expressed aggregation instructions.

Contents

Definition

Formally an  \ OWA operator of dimension  \ n is a mapping  F: R_n \rightarrow R that has an associated collection of weights  \  W = [w_1, ..., w_n] lying in the unit interval and summing to one and with

 F(a_1, ..., a_n) =  \sum_{j=1}^n  w_j b_j where bj is the jth largest of the ai

By choosing different W we can implement different aggregation operators. The OWA operator is a non-linear operator as a result of the process of determining the bj.

Properties

The OWA operator is a mean operator. It is bounded, monotonic, symmetric, and idempotent, as defined below.

Bounded    Min(a_1, ..., a_n) \le F(a_1, ..., a_n) \le Max(a_1, ..., a_n)
Monotonic    F(a_1, ..., a_n) \ge F(g_1, ..., g_n) if  a_i \ge g_i for \ i = 1,2,...,n
Symmetric    F(a_1, ..., a_n)  = F(a_\boldsymbol{\pi(1)}, ..., a_\boldsymbol{\pi(n)}) if \boldsymbol{\pi} is a permutation map
Idempotent   \ F(a_1, ..., a_n)  =  a     if all  \ a_i = a

Notable OWA Operators

 \ F(a_1, ..., a_n) = Max(a_1, ..., a_n) if  \ w_1 = 1 and  \ w_j = 0 for  j \ne 1
 \ F(a_1, ..., a_n) = Min(a_1, ..., a_n) if  \ w_n = 1 and  \ w_j = 0 for  j \ne n

Characterizing Features

Two features have been used to characterize the OWA operators. The first is the attudinal character(orness).

This is defined as

A-C(W) = A-C(W) =    \frac{1}{n-1} \sum_{j=1}^n (n - j) w_j.

It is known that  A-C(W) \in [0, 1] .

In addition A-C(Max) = 1, A-C(Ave) = A-C(Med) = 0.5 and A–C(Min) = 0. Thus the A-C goes from 1 to 0 as we go from Max to Min aggregation. The attitudinal character characterizes the similarity of aggregation to OR operation(OR is defined as the Max).

The second feature is the dispersion. This defined as

H(W) =   -\sum_{j=1}^n w_j \ln (w_j).

An alternative definition is E(W) = \sum_{j=1}^n w_j^2 . The dispersion characterizes how uniformly the arguments are being used

Type-1 OWA Aggregation Operators

The above Yager's OWA operators are used to aggregate the crisp values. Can we aggregate fuzzy sets in the OWA mechanism ? The type-1 OWA operators have been proposed for this purpose. So the type-1 OWA operators provides us with a new technique for directly aggregating uncertain information with uncertain weights via OWA mechanism in soft decision making and data mining, where these uncertain objects are modelled by fuzzy sets.

The type-1 OWA operator is defined according to the alpha-cuts of fuzzy sets as follows:

Given the n linguistic weights \left\{ {W^i} \right\}_{i =1}^n in the form of fuzzy sets defined on the domain of discourse U = [0,\;\;1], then for each \alpha \in [0,\;1], an α-level type-1 OWA operator with α-level sets \left\{ {W_\alpha ^i } \right\}_{i = 1}^n to aggregate the α-cuts of fuzzy sets \left\{ {A^i} \right\}_{i =1}^n is given as


\Phi_\alpha \left( {A_\alpha ^1 , \cdots ,A_\alpha ^n } \right) =\left\{ {\frac{\sum\limits_{i = 1}^n {w_i a_{\sigma (i)} } }{\sum\limits_{i = 1}^n {w_i } }\left| {w_i \in W_\alpha ^i ,\;a_i } \right. \in A_\alpha ^i ,\;i = 1, \cdots ,n} \right\}

where W_\alpha ^i= \{w| \mu_{W_i }(w) \geq \alpha \}, A_\alpha ^i=\{ x| \mu _{A_i }(x)\geq \alpha \}, and \sigma :\{\;1, \cdots ,n\;\} \to \{\;1, \cdots ,n\;\} is a permutation function such that a_{\sigma (i)} \ge a_{\sigma (i + 1)} ,\;\forall \;i = 1, \cdots ,n - 1, i.e., aσ(i) is the ith largest element in the set \left\{ {a_1 , \cdots ,a_n } \right\}.

The computation of the type-1 OWA output is implemented by computing the left end-points and right end-points of the intervals \Phi _\alpha \left( {A_\alpha ^1 , \cdots ,A_\alpha ^n } \right): \Phi _\alpha \left( {A_\alpha ^1 , \cdots ,A_\alpha ^n } \right)_ {-} and 
\Phi _\alpha \left( {A_\alpha ^1 , \cdots ,A_\alpha ^n } \right)_ {+}, where A_\alpha ^i=[A_{\alpha-}^i, A_{\alpha+}^i], W_\alpha ^i=[W_{\alpha-}^i, W_{\alpha+}^i]. Then membership function of resulting aggregation fuzzy set is:

\mu _{G} (x) = \mathop \vee \limits_{\alpha :x \in \Phi _\alpha \left( {A_\alpha ^1 , \cdots
,A_\alpha ^n } \right)_\alpha } \alpha

For the left end-points, we we need to solve the following programming problem:

 \Phi _\alpha \left( {A_\alpha ^1 , \cdots ,A_\alpha ^n } \right)_{-} = \mathop {\min }\limits_{\begin{array}{l} W_{\alpha - }^i \le w_i \le W_{\alpha + }^i A_{\alpha - }^i \le a_i \le A_{\alpha + }^i  \end{array}} \sum\limits_{i = 1}^n {w_i a_{\sigma (i)} / \sum\limits_{i = 1}^n {w_i } }

while for the right end-points, we need to solve the following programming problem:

\Phi _\alpha \left( {A_\alpha ^1 , \cdots , A_\alpha ^n } \right)_{+} = \mathop {\max }\limits_{\begin{array}{l} W_{\alpha - }^i \le w_i \le W_{\alpha + }^i  A_{\alpha - }^i \le a_i \le A_{\alpha + }^i  \end{array}} \sum\limits_{i = 1}^n {w_i a_{\sigma (i)} / \sum\limits_{i =
1}^n {w_i } }

The paper has presented a fast method to solve two programming problem so that the type-1 OWA aggregation operation can be performed efficiently.

References

[1]. Yager, R. R., "On ordered weighted averaging aggregation operators in multi-criteria decision making," IEEE Transactions on Systems, Man and Cybernetics 18, 183-190, 1988.

[2]. Yager, R. R. and Kacprzyk, J., The Ordered Weighted Averaging Operators: Theory and Applications, Kluwer: Norwell, MA, 1997.

[3]. Liu, X., "The solution equivalence of minimax disparity and minimum variance problems for OWA operators," International Journal of Approximate Reasoning 45, 68-81, 2007.

[4]. Emrouznejad (2009) SAS/OWA: ordered weighted averaging in SAS optimization, Soft Computing [1]

[5]. Torra, V. and Narukawa, Y., Modeling Decisions: Information Fusion and Aggregation Operators, Springer: Berlin, 2007.

[6]. Majlender, P., "OWA operators with maximal Rényi entropy," Fuzzy Sets and Systems 155, 340-360, 2005.

[7]. Szekely, G. J. and Buczolich, Z., " When is a weighted average of ordered sample elements a maximum likelihood estimator of the location parameter?" Advances in Applied Mathematics 10, 1989, 439-456.

[8].S. -M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, "Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers," Fuzzy Sets and Systems, Vol.159, No.24, pp.3281-3296, 2008 [2]

[9].S. -M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, "Alpha-level aggregation: a practical approach to type-1 OWA operation for aggregating uncertain information with applications to breast cancer treatments," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no.10, 2011, pp.1455 - 1468.[3]

[10].S. -M. Zhou, R. I. John, F. Chiclana and J. M. Garibaldi, "On aggregating uncertain information by type-2 OWA operators for soft decision making," International Journal of Intelligent Systems, vol. 25, no.6, pp. 540-558, 2010.[4]


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