基于信息熵的专家聚类赋权方法

鉴于群组决策专家赋权方法研究中,现有赋权方法虽然考虑了专家给出的排序向量的一致性,但缺乏对排序向量信息相似性的度量,导致可能出现排序向量与群体共识相近,但信息不确定性较大的专家被赋予了与其他专家相同权重的问题.基于此,提出一种基于信息熵的专家聚类赋权方法,运用信息相似系数对排序向量进行聚类分析,根据聚类结果和排序向量的...

Method for determining experts’ weights based on entropy and cluster analysis

According to the methods of determining experts’ weights in group decision-making, the existing methods take
into account the consistency of experts’ collating vectors, but it is lack of the measure of its information similarity. So it
may occur that although the collating vector is similar to the group consensus, information uncertainty is great of a certain
expert. However, it is given the same weight to the other experts. For this, a method for deriving experts’ weights based on
entropy and cluster analysis is proposed, in which the collating vectors of all experts are classified with information similarity
coefficient, and the experts’ weights are determined according to the result of classification and entropy of collating vectors.
Finally, a numerical example shows that the method is effective and feasible.