Geo-uninorm consistency control module for preference similarity network hierarchical clustering based consensus model
Geo-uninorm consistency control module for preference similarity network hierarchical clustering based consensus model
复制标题
DOI：
10.1016/j.knosys.2018.05.039
复制
发表时间：
2018-05
期刊：
影响因子：
--
通讯作者：
N. Kamis;F. Chiclana;J. Levesley
中科院分区：
文献类型：
--
作者：
N. Kamis;F. Chiclana;J. Levesley
在群体决策过程中，为了避免产生误导性的决策方案，除了保证专家组接受最终的决策方案显然需要达成共识外，还应追求信息的一致性。对于以互易模糊偏好关系表示的专家偏好，一致性与传递性有关。在本研究中，我们提出了一种新的解决具有互偏好关系的GDM的共识方法，该方法基于传递性实现了一致性的合理性准则，在找到最终决策解之前具有以下两个目标：(a)开发一致性控制模块，为GDM问题中不一致的专家提供个性化的一致性反馈，以保证偏好的一致性；(B)基于具有无向完全链接的无向加权一致偏好相似网络结构设计基于一致性偏好网络聚类的共识度量，该结构使用结构等价的概念将允许人们(i)聚类专家；（ii）衡量他们的共识状态。基于互向偏好关系一致性的一致特征和几何平均，我们提出了从给定的互向偏好关系中导出基于一致性的偏好关系的geo- uniform算子的实现。这随后用于衡量给定偏好关系的一致性水平，作为各自关系的偏好强度基本向量之间的余弦相似性。拟议的地理统一一致性措施将允许建立一个基于个性化反馈机制的一致性控制模块，以便在一致性水平不足时实施。这种一致性控制模块有两个优点：(1)它通过建议不一致的专家以最小的变化修改他们的偏好来保证一致性；(2)它根据专家个人的不一致程度，单独提供公平的建议。在保证偏好一致性的前提下，构造结构等价偏好相似网络。为了表示结构等效的专家和衡量专家群体内的共识，我们开发了一种基于凝聚层次聚类的共识算法，该算法可以作为一种可视化工具，用于监测专家群体协议的现状和控制决策过程。通过与现有文献研究的对比分析，验证了所提出的模型，并从中得出结论并进行了解释。
In order to avoid misleading decision solutions in group decision making (GDM) processes, in addition to consensus, which is obviously desirable to guarantee that the group of experts accept the final decision solution, consistency of information should also be sought after. For experts’ preferences represented by reciprocal fuzzy preference relations, consistency is linked to the transitivity property. In this study, we put forward a new consensus approach to solve GDM with reciprocal preference relations that implements rationality criteria of consistency based on the transitivity property with the following twofold aim prior to finding the final decision solution: (A) to develop aconsistency control moduleto provide personalized consistency feedback to inconsistent experts in the GDM problem to guarantee the consistency of preferences; and (B) to design aconsistent preference network clustering based consensus measurebased on an undirected weighted consistent preference similarity network structure with undirected complete links, which using the concept of structural equivalence will allow one to (i) cluster the experts; and (ii) measure their consensus status. Based on the uninorm characterization of consistency of reciprocal preferences relations and the geometric average, we propose the implementation of the geo-uninorm operator to derive a consistent based preference relation from a given reciprocal preference relation. This is subsequently used to measure the consistency level of a given preference relation as the cosine similarity between the respective relations’ essential vectors of preference intensity. The proposed geo-uninorm consistency measure will allow the building of a consistency control module based on a personalized feedback mechanism to be implemented when the consistency level is insufficient. This consistency control module has two advantages: (1) it guarantees consistency by advising inconsistent expert(s) to modify their preferences with minimum changes; and (2) it providesfairrecommendations individually, depending on the experts’ personal level of inconsistency. Once consistency of preferences is guaranteed, a structural equivalence preference similarity network is constructed. For the purpose of representing structurally equivalent experts and measuring consensus within the group of experts, we develop an agglomerative hierarchical clustering based consensus algorithm, which can be used as a visualization tool in monitoring current state of experts’ group agreement and in controlling the decision making process. The proposed model is validated with a comparative analysis with an existing literature study, from which conclusions are drawn and explained.