 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 operator of dimension is a mapping that has an associated collection of weights lying in the unit interval and summing to one and with
 where b_{j} is the j^{th} largest of the a_{i}
By choosing different W we can implement different aggregation operators. The OWA operator is a nonlinear operator as a result of the process of determining the b_{j}.
Properties
The OWA operator is a mean operator. It is bounded, monotonic, symmetric, and idempotent, as defined below.
Bounded Monotonic if for Symmetric if is a permutation map Idempotent if all Notable OWA Operators
 if and for
 if and for
Characterizing Features
Two features have been used to characterize the OWA operators. The first is the attudinal character(orness).
This is defined as
It is known that .
In addition AC(Max) = 1, AC(Ave) = AC(Med) = 0.5 and A–C(Min) = 0. Thus the AC 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
An alternative definition is The dispersion characterizes how uniformly the arguments are being used
Type1 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 type1 OWA operators have been proposed for this purpose. So the type1 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 type1 OWA operator is defined according to the alphacuts of fuzzy sets as follows:
Given the n linguistic weights in the form of fuzzy sets defined on the domain of discourse , then for each , an αlevel type1 OWA operator with αlevel sets to aggregate the αcuts of fuzzy sets is given as
where , and is a permutation function such that , i.e., a_{σ(i)} is the ith largest element in the set .
The computation of the type1 OWA output is implemented by computing the left endpoints and right endpoints of the intervals : and where . Then membership function of resulting aggregation fuzzy set is:
For the left endpoints, we we need to solve the following programming problem:
while for the right endpoints, we need to solve the following programming problem:
The paper has presented a fast method to solve two programming problem so that the type1 OWA aggregation operation can be performed efficiently.
References
[1]. Yager, R. R., "On ordered weighted averaging aggregation operators in multicriteria decision making," IEEE Transactions on Systems, Man and Cybernetics 18, 183190, 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, 6881, 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, 340360, 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, 439456.
[8].S. M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, "Type1 OWA operators for aggregating uncertain information with uncertain weights induced by type2 linguistic quantifiers," Fuzzy Sets and Systems, Vol.159, No.24, pp.32813296, 2008 [2]
[9].S. M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, "Alphalevel aggregation: a practical approach to type1 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 type2 OWA operators for soft decision making," International Journal of Intelligent Systems, vol. 25, no.6, pp. 540558, 2010.[4]
Wikimedia Foundation. 2010.
Look at other dictionaries:
Owa — or OWA may refer to: Owa language, a language of the Solomon Islands Ōwa, an era in Japanese history Acronyms Open World Assumption, formal reasoning with incomplete knowledge Ordered Weighted Averaging (OWA) Aggregation Operators Outlook Web App … Wikipedia
OWA — may refer to:* Ordered Weighted Averaging (OWA) Aggregation Operators * Outlook Web Access * Open Wireless Architecture … Wikipedia
Category:Logic in computer science — Logic in computer science is that branch of mathematical logic which is approximately the intersection between mathematical logic and computer science. It contains: Those investigations into logic that are guided by applications in computer… … Wikipedia
Outline of artificial intelligence — The following outline is provided as an overview of and topical guide to artificial intelligence: Artificial intelligence (AI) – branch of computer science that deals with intelligent behavior, learning, and adaptation in machines. Research in AI … Wikipedia