A probabilistic model for linguistic multi-expert decision making involving semantic overlapping
A probabilistic model for linguistic multi-expert decision making involving semantic overlapping
复制标题
DOI：
10.1016/j.eswa.2011.01.105
复制
发表时间：
2011-07
期刊：
影响因子：
--
通讯作者：
Hongbin Yan;V. Huynh;Y. Nakamori
中科院分区：
文献类型：
--
作者：
Hongbin Yan;V. Huynh;Y. Nakamori
语言多专家决策的主要目标是利用多个专家提供的语言判断来选择最佳方案。本文提出了一种语言MEDM的概率模型，该模型能够处理语言聚合中的语义重叠和选择函数中的决策者偏好信息。在语言聚合阶段，每个语言判断的正确性由一组语言标签上的可能性分布来捕获。在基本模型中引入了一个置信度参数来表示专家的置信度。这种语言聚合的基本思想是将一个可能性分布转换成与其相关联的概率分布。所提出的语言聚合的结果在一组具有概率分布的标签。作为一个选择函数，提出了一个面向目标的排序方法，这意味着决策者是满意的选择一个替代品作为最好的，如果它的性能至少是“好”的，他的要求。与以往的研究进行了比较分析，显示我们的模型的优势，通过一个例子从文献中借用。我们的模型的主要优点是它能够处理具有部分语义重叠的语言标签，以及将专家和决策者的偏好。
The main objective of linguistic multi-expert decision making (MEDM) is to select the best alternative(s) using linguistic judgements provided by multiple experts. This paper presents a probabilistic model for linguistic MEDM, which is able to deal with semantic overlapping in linguistic aggregation and decision-makers’ preference information in choice function. In linguistic aggregation phase, the vagueness of each linguistic judgement is captured by a possibility distribution on a set of linguistic labels. A confidence parameter is also incorporated into the basic model to model experts’ confidence degree. The basic idea of this linguistic aggregation is to transform a possibility distribution into its associated probability distribution. The proposed linguistic aggregation results in a set of labels having a probability distribution. As a choice function, a target-oriented ranking method is proposed, which implies that the decision-maker is satisfactory to choose an alternative as the best if its performance is as at least “good” as his requirements. A comparative analysis with prior research is also given to show the advantages of our model via an example borrowed from the literature. The main advantage of our model is its capacity to deal with linguistic labels having partial semantic overlapping as well as incorporate experts and decision-makers’ preferences.