Understanding the large family of Dempster-Shafer theory's fusion operators - a decision-based measure

C. Osswald, Arnaud Martin
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引用次数: 50

Abstract

Distances between fusion operators are measured using a class of random belief functions. With similarity analysis, the structure of this family is extracted, for two and three information sources. The conjunctive operator, quick and associative but very isolated on a large discernment space, and the arithmetic mean are identified as outliers, while the hybrid method and six proportional conflict-redistributing rules (PCR) form a continuum. The hybrid method is showed as being central for the family of fusion methods. All the fusion operators tested with random belief functions are validated on the fusion of radar data classifiers, and show the interest of some new PCR methods
理解Dempster-Shafer理论的聚变算子大家族——一个基于决策的度量
利用一类随机信念函数测量融合算子之间的距离。通过相似性分析,分别对两个信息源和三个信息源提取了该家族的结构。结合算子具有快速联想性,但在较大的识别空间上非常孤立,它与算术平均值被识别为离群值,而混合方法与6个比例冲突再分配规则(PCR)形成连续体。混合方法被认为是融合方法家族的核心。用随机信念函数测试的融合算子在雷达数据分类器的融合上得到了验证,显示了一些新的PCR方法的兴趣
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