In group decision making (GDM), the quality of the solution relies primarily on the quality and the expertize of decision makers. At that point, deriving the weights, which reflects their importance or perceived reliability of decision makers, presents as a new challenge. In addition to that, the uncertainty is also a common problem for GDM. These problems are also faced in the sensor fusion problem where information from multiple sources must be aggregated. Therefore, in this study, a large scale GDM approach for sensor fusion is proposed. Since the proposed method is a clustering‐based method, it provides acceptable results in the sensor networks consisting of multiple sensors. It can work under uncertainty as a result of converting the raw data obtained from sensors to the basic probability assignments. It also considers the reliability of the sensors clusters by assigning three objective weights. In addition to these objective weights, the proposed method enables to assign subjective weights to integrate supervisors/intelligence analyst experiences and knowledge in the problem field. The applicability and the validity of the proposed method are checked through two real classification data sets: ionosphere and forest type mapping data set. Experiments show that the classification rate is increased significantly when the proposed method is applied to two data sets. Finally, effect of extension parameter, objective weights, reliability threshold, number of clusters and clustering method on the classification rate and the detection probability are examined, and future studies are provided in conclusion.

中文翻译：

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基于 Dempster-Shafer 证据理论的传感器融合使用大规模群决策方法

在群体决策 (GDM) 中，解决方案的质量主要取决于决策者的质量和专业知识。在这一点上，得出反映决策者重要性或感知可靠性的权重是一项新的挑战。除此之外，不确定性也是 GDM 的常见问题。在传感器融合问题中也面临这些问题，其中必须聚合来自多个来源的信息。因此，在本研究中，提出了一种用于传感器融合的大规模 GDM 方法。由于所提出的方法是基于聚类的方法，因此它在由多个传感器组成的传感器网络中提供了可接受的结果。由于将从传感器获得的原始数据转换为基本概率分配，因此它可以在不确定性下工作。它还通过分配三个客观权重来考虑传感器集群的可靠性。除了这些客观权重之外，所提出的方法还能够分配主观权重，以整合问题领域的主管/智能分析师的经验和知识。通过两个真实的分类数据集：电离层和森林类型映射数据集来检验所提出方法的适用性和有效性。实验表明，当该方法应用于两个数据集时，分类率显着提高。最后，考察了扩展参数、客观权重、可靠性阈值、聚类数和聚类方法对分类率和检测概率的影响，并对未来的研究进行了总结。