Learning Weights in the Generalized OWA Operators
نویسندگان
چکیده
منابع مشابه
Learning Weights in the Generalized OWA Operators
This paper discusses identification of parameters of generalized ordered weighted averaging (GOWA) operators from empirical data. Similarly to ordinary OWA operators, GOWA are characterized by a vector of weights, as well as the power to which the arguments are raised. We develop optimization techniques which allow one to fit such operators to the observed data. We also generalize these methods...
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Different types of aggregation operators such as the ordered weighted quasi-arithmetic mean (Quasi-OWA) operator and the normalized Hamming distance are studied. We introduce the use of the OWA operator in generalized distances such as the quasiarithmetic distance. We will call these new distance aggregation the ordered weighted quasi-arithmetic distance (Quasi-OWAD) operator. We develop a gene...
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We study the determination of weights for two types of aggregation operators: the weighted mean and the OWA operator. We assume that there is at our disposal a set of examples for which the outcome of the aggregation operator is known. In the case of the OWA operator, we compare the results obtained by our method with another one in the literature. We show that the optimal weighting vector is r...
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Actual result of aggregation performed by an ordered weighted averaging (OWA) operator heavily depends upon the weighting vector used. A number of approaches for obtaining the associated weights have been suggested in the academic literature. In this paper, we present a method for determining the OWA weights when (1) the preferences of some subset of alternatives over other subset of alternativ...
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ژورنال
عنوان ژورنال: Fuzzy Optimization and Decision Making
سال: 2005
ISSN: 1568-4539,1573-2908
DOI: 10.1007/s10700-004-5868-3