Ahn Byeong Seok
College of Business Administration, Chung-Ang University, Seoul, Korea.
IEEE Trans Syst Man Cybern B Cybern. 2008 Apr;38(2):540-6. doi: 10.1109/TSMCB.2007.912743.
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.
量词引导的聚合用于聚合多标准输入。因此，选择合适的量词在多标准聚合中至关重要，因为聚合权重是由所选量词生成的。自从Yager提出一种通过用于量词引导聚合的三个相对量词来获得有序加权平均（OWA）向量的方法以来，在开发适用于多标准聚合的新量词方面投入的精力有限。在本通信中，我们提出了一些新的量词函数，这些函数基于加权函数，其特征是显示出与聚合标准数量无关的恒定的偏度值。所提出的正则递增单调量词和正则递减单调量词产生的偏度与每个量词函数所源自的加权函数相同。此外，量词偏度迅速收敛到具有恒定偏度值的加权函数的偏度值。这一结果表明，在标准数量不太少的情况下，量词引导的OWA聚合将产生类似的聚合结果。
