Consensus measures constructed from aggregation functions and fuzzy implications

被引：52

作者：

Beliakov, Gleb ^{[1
]}

Calvo, Tomasa ^{[2
]}

James, Simon ^{[1
]}

机构：

[1] Deakin Univ, Sch Informat Technol, Burwood, Vic 3125, Australia

[2] Univ Alcala de Henares, Dept Comp Sci, Alcala De Henares 28871, Spain

来源：

KNOWLEDGE-BASED SYSTEMS | 2014年 / 55卷

关键词：

Aggregation functions; Consensus; Fuzzy implications; Group decision making; Bonferroni mean; GROUP DECISION-MAKING; SUPPORT-SYSTEM; MODEL; DIFFERENCE;

D O I：

10.1016/j.knosys.2013.07.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We focus on the problem of constructing functions that are able to measure the degree of consensus for a set of inputs provided over the unit interval. When making evaluations based on inputs from multiple criteria, sources or experts, the resulting output can be seen as the value which best represents the individual contributions. However it may also be desirable to know the extent to which the inputs agree. Does the representative value reflect a universal opinion? Or has there been a high degree of tradeoff? We consider the properties relating to such consensus measures and propose two general models built component-wise from aggregation functions and fuzzy implications. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：1 / 8

页数：8

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