Some Quantifier Functions From Weighting Functions With Constant Value of Orness
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)Published 13 March 2008
Byeong Seok Ahn
Citations21
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TL;DR
This correspondence proposes some new quantifier functions that are based on the weighting functions characterized by showing a constant value of orness independent of the number of criteria aggregated, and indicates that a quantifier-guided OWA aggregation will result in a similar aggregate in case thenumber of criteria is not too small.
Abstract
The quantifier-guided aggregation is used for aggregating the multiple-criteria input. Therefore, the selection of appropriate quantifiers is crucial in multicriteria aggregation since the weights for the aggregation are generated from the selected quantifier. Since Yager proposed a method for obtaining the ordered weighted averaging (OWA) vector via the three relative quantifiers used for the quantifier-guided aggregation, limited efforts have been devoted to developing new quantifiers that are suitable for use in multicriteria aggregation. In this correspondence, we propose some new quantifier functions that are based on the weighting functions characterized by showing a constant value of orness independent of the number of criteria aggregated. The proposed regular increasing monotone and regular decreasing monotone quantifiers produce the same orness as the weighting functions from which each quantifier function originates. Further, the quantifier orness rapidly converges into the value of orness of the weighting functions having a constant value of orness. This result indicates that a quantifier-guided OWA aggregation will result in a similar aggregate in case the number of criteria is not too small.
Keywords
Computer ScienceDecision SciencesMathematics
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