Results 151 to 160 of about 70,164 (185)
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MP-OWA: The most preferred OWA operator
Knowledge-Based Systems, 2008In practical term any result obtained using an ordered weighted averaging (OWA) operator heavily depends upon the method to determine the weighting vector. Several approaches for obtaining the associated weights have been suggested in the literature, in which none of them took into account the preference of alternatives.
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IEEE Transactions on Fuzzy Systems, 2015
A critical issue when selecting an ordered weighted aggregation (OWA) operator is the determination of the associated weights. For this reason, numerous weight generating methods have appeared in the literature. In this paper, a generalization of the binomial OWA operator on the basis of the Stancu polynomial is proposed and analyzed.
Amit K. Singh +2 more
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A critical issue when selecting an ordered weighted aggregation (OWA) operator is the determination of the associated weights. For this reason, numerous weight generating methods have appeared in the literature. In this paper, a generalization of the binomial OWA operator on the basis of the Stancu polynomial is proposed and analyzed.
Amit K. Singh +2 more
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Mm-OWA: A Generalization of OWA Operators
IEEE Transactions on Fuzzy Systems, 2018We characterize those operators that satisfy the properties of monotonicity, permutation invariance, positive homogeneity, and translation invariance. As these operators do not necessarily satisfy comonotonic additivity, their class is larger than that of ordered weighted averaging (OWA) operators.
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Generalized OWA Aggregation Operators
Fuzzy Optimization and Decision Making, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Classification with fuzzy OWA distance
2014 International Conference on Fuzzy Theory and Its Applications (iFUZZY2014), 2014OWA (Ordered Weighted Averaging) Distance Based CxK Nearest Neighbor Algorithm (CxK-NN) via L-R fuzzy data is performed with two different fuzzy metric measures. We use fuzzy metric defined by Diamond and a weighted dissimilarity measure composed by spread distances and center distances in order to evaluate the effects of different metric measures.
Ulutagay, Gozde, Kantarci, Suzan
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RECURSIVE AND ITERATIVE OWA OPERATORS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2005An important issue when using the OWA aggregation operators is the determination of weights. One approach is to link the weights to a desired attitudinal character for the aggregation. The ME-OWA operators provide a pioneering example of this approach.
Troiano, Luigi, Yager, Ronald R.
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AN INTUITIONISTIC OWA OPERATOR
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004The OWA (Ordered Weighted Average) operator is a powerful non-linear operator for aggregating a set of inputs ai,i∈{1,2,…,M}. In the original OWA operator the inputs are crisp variables ai. This restriction was subsequently removed by Mitchell and Schaefer who by application of the extension principle defined a fuzzy OWA operator which aggregates a ...
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Fuzzy Sets and Systems, 1996
Abstract The ordered weighted averaging operators are introduced and some of their properties described. Attention is then focused on the problem of maximizing an OWA aggregation of a group of variables that are interrelated and constrained by a collection of linear inequalities.
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Abstract The ordered weighted averaging operators are introduced and some of their properties described. Attention is then focused on the problem of maximizing an OWA aggregation of a group of variables that are interrelated and constrained by a collection of linear inequalities.
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On the Comparisons of OWA Operators and Ordinal OWA Operators
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1997In this paper, comparisons of OWA operators and ordinal OWA operators are carried out by using lattice theoretic methods. It is proved first that in both cases, the set of all aggregation operators forms a lattice, then the concept of positive valuation is used to measure the "orness" of aggregation operators and the structures of all such possible ...
Taihe Fan, Dan A. Ralescu
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The equivalence of maximum entropy OWA operator and geometric OWA operator
Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), 2004In this paper, some properties of geometric OWA operator are investigated. The equivalence of the geometric OWA operator and the Maximum Entropy OWA operator is proved.
null Xin-Wang Liu, null Liang-Hua Chen
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