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The strength of coalition in a dispersed decision support system with negotiations

机译:联合谈判在分散的决策支持系统中的实力

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摘要

Issues related to decision making based on dispersed knowledge are discussed in the paper. A system using a process of combining classifiers into coalitions is used. In this article clusters that are generated using an approach with a negotiation stage are used. Such clusters are more complex and are better able to reconstruct the views of the agents on the classifications. However, a significant improvement is not obtained when we use these clusters without an additional enhancement to the method of conflict analysis. In order to take full advantage of the clustering method, the size and structure of the clusters should be taken into account. Therefore, the main aim of this paper is to examine the impact of the method of conflict analysis and the methods that are used to determine the individual weights of the clusters on the effectiveness of the inference in a system that has a negotiation stage. Four new methods for determining the strength of a coalition are proposed and compared. The tests, which were performed on data from the University of California, Irvine Repository, are presented. The results that were obtained are much better than in the case in which the strength of the clusters was not calculated. The approach that consists in the computation of the individual weights from the judgments of each cluster allowed the size and structure of the clusters to be taken into account. This in turn allowed us to take full advantage of a clustering method with a negotiation stage. (C) 2016 Elsevier B.V. All rights reserved.
机译:本文讨论了与基于分散知识的决策有关的问题。使用了一种使用将分类器组合成联盟的过程的系统。在本文中,将使用通过协商阶段的方法生成的集群。这样的集群更加复杂,并且能够更好地重构代理对分类的看法。但是,如果不使用冲突分析方法的其他增强功能而使用这些群集,则无法获得显着的改进。为了充分利用聚类方法,应考虑聚类的大小和结构。因此,本文的主要目的是研究冲突分析方法以及用于确定集群权重的方法对具有协商阶段的系统中推理有效性的影响。提出并比较了四种确定联盟实力的新方法。介绍了根据加利福尼亚大学尔湾分校的数据进行的测试。与未计算簇的强度的情况相比,获得的结果要好得多。根据每个聚类的判断来计算各个权重的方法允许考虑聚类的大小和结构。反过来,这使我们可以充分利用带有协商阶段的聚类方法。 (C)2016 Elsevier B.V.保留所有权利。

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