An extended minimax disparity to determine the OWA operator weights
Computers and Industrial Engineering
Notes on properties of the OWA weights determination model
Computers and Industrial Engineering
Orness and parameterized RIM quantifier aggregation with OWA operators: A summary
International Journal of Approximate Reasoning
A general model of parameterized OWA aggregation with given orness level
International Journal of Approximate Reasoning
On the equivalence of some approaches to the OWA operator and RIM quantifier determination
Fuzzy Sets and Systems
MP-OWA: The most preferred OWA operator
Knowledge-Based Systems
On Decision Support Under Risk by the WOWA Optimization
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
WOWA Enhancement of the Preference Modeling in the Reference Point Method
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
A generalized model for prioritized multicriteria decision making systems
Expert Systems with Applications: An International Journal
Parameterized defuzzification with continuous weighted quasi-arithmetic means - An extension
Information Sciences: an International Journal
On efficient WOWA optimization for decision support under risk
International Journal of Approximate Reasoning
Improving minimax disparity model to determine the OWA operator weights
Information Sciences: an International Journal
The orness measures for two compound quasi-arithmetic mean aggregation operators
International Journal of Approximate Reasoning
Determining more realistic OWA weights
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
On optimization of the importance weighted OWA aggregation of multiple criteria
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Parameterized OWA operator weights: An extreme point approach
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Nearest-neighbor guided evaluation of data reliability and its applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Models to determine parameterized ordered weighted averaging operators using optimization criteria
Information Sciences: an International Journal
Maximum Bayesian entropy method for determining ordered weighted averaging operator weights
Computers and Industrial Engineering
Parameterized approximation of fuzzy number with minimum variance weighting functions
Mathematical and Computer Modelling: An International Journal
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Induced continuous Choquet integral operators and their application to group decision making
Computers and Industrial Engineering
An analytic approach to obtain the least square deviation OWA operator weights
Fuzzy Sets and Systems
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Based on the researches on ordered weighted average (OWA) operator, the weighted OWA operator (WOWA) and especially the quantifier guided aggregation method, with the generating function representation of regular increasing monotone (RIM) quantifier technique, we discuss the properties of WOWA operator with RIM quantifier in the respect of orness. With the continuous OWA and WOWA ideas recently proposed by Yager, an improvement on the continuous OWA and WOWA operator is proposed. The properties of WOWA are also extended from discrete to the continuous case. Based on these properties, two families of parameterized RIM quantifiers for WOWA operator are proposed, which have exponential generating function and piecewise linear generating function respectively. One interesting property of these two kinds of RIM quantifiers is that for any aggregated set (or variable) under any weighted (distribution) function, the aggregation values are always consistent with the orness (optimistic) levels, so they can be used to represent the decision maker's preference, and we can get the preference value of fuzzy sets or random variables with the orness level of RIM quantifier as their control parameter.