分类问题中群体决策支持的方法和多标准算法

Georgios Rigopoulos
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引用次数: 0

摘要

在这项工作中,介绍了一种适用于小型协作团队的小组决策方法和算法。它以分类决策的多标准算法为基础,在参数层面对成员的偏好进行汇总。该算法适用于由流程促进者指导的结构相对完善的问题。最初,主持人向小组提出一组参数,接下来小组成员对提出的参数集进行评估,并以数字或语言形式表达他们的偏好。通过适当的运算符对个人偏好进行汇总,生成一组小组参数值,并将其作为分类算法的输入。NeXClass 多标准分类算法用于对备选方案进行分类,最初是对一组训练备选方案进行分类,随后对整组备选方案进行分类。最后,小组成员对结果进行评估,并计算出共识和满意度指标。如果接受度较低,则由主持人对问题参数进行审查,并重复汇总阶段。该方法是处理群体决策问题的有效方法,可用于多种商业环境。鉴于目前在各种环境中大量使用基于代理的决策,该算法也可被多代理环境中的软件代理用于自动决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methodology and Multicriteria Algorithm for Group Decision Support in Classification Problems
In this work, a Group Decision methodology and algorithm for small collaborating teams is introduced. It is based on a multicriteria algorithm for classification decisions, where aggregation of member preferences is executed at the parameter level. The algorithm applies to relatively well-structured problems guided by a process facilitator. Initially, a set of parameters is proposed by the facilitator to the group and next group members evaluate the proposed parameter set and express their preferences in numeric or linguistic format. Individual preferences are aggregated by appropriate operators, and a set of group parameter values is generated, which is used as input for the classification algorithm. NeXClass multicriteria classification algorithm is used for the classification of alternatives, initially at a training set of alternatives and later at the entire set. Finally, group members evaluate results, and consensus, as well as satisfaction metrics, are calculated. In case of a low acceptance level, problem parameters are reviewed by the facilitator, and the aggregation phase is repeated. The methodology is a valid approach for group decision problems and can be utilized in numerous business environments. The algorithm can be also utilized by software agents in multiagent environments for automated decision-making, given the large volume of agent-based decision-making in various settings today.
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