Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT.
Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT.
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基于复杂Pignistic变换的物联网医疗诊断多源信息融合证据距离
DOI：
10.3390/s21030840
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发表时间：
2021-01-27
期刊：
影响因子：
--
通讯作者：
Xiao F
中科院分区：
文献类型：
--
作者：
Xiao F
在过去的几十年里，多传感器信息融合受到了广泛的关注，特别是对于智能物联网（IoT）。由于设备、外部环境和通信问题的影响，收集到的信息可能是不确定的、不精确的，甚至是相互冲突的。如何处理这种不确定性仍然是一个悬而未决的问题。复杂证据理论（CET）是处理物联网多传感器信息融合中不确定性问题的有效方法。然而，在CET中，如何测量复杂基信念分配（CBBA）之间的距离以管理冲突仍然是一个悬而未决的问题，这有利于提高物联网融合过程中的性能。因此，本文首先提出了一种复Pignistic变换函数来对复质量函数进行变换，然后提出了一种广义投注承诺距离（BCD）来度量CBBA在CET中的差异。建议的BCD是一个广义模型，以提供更多的能力来衡量CBBA之间的差异。此外，还分析了BCD码的其他性质，包括非负性、非退化性、对称性和三角不等式。此外，基于新定义的BCD，设计了一种多属性决策的基算法及其加权扩展。最后，将这些决策算法应用于智能物联网环境下的医疗诊断问题，以揭示其有效性。
Multisource information fusion has received much attention in the past few decades, especially for the smart Internet of Things (IoT). Because of the impacts of devices, the external environment, and communication problems, the collected information may be uncertain, imprecise, or even conflicting. How to handle such kinds of uncertainty is still an open issue. Complex evidence theory (CET) is effective at disposing of uncertainty problems in the multisource information fusion of the IoT. In CET, however, how to measure the distance among complex basis belief assignments (CBBAs) to manage conflict is still an open issue, which is a benefit for improving the performance in the fusion process of the IoT. In this paper, therefore, a complex Pignistic transformation function is first proposed to transform the complex mass function; then, a generalized betting commitment-based distance (BCD) is proposed to measure the difference among CBBAs in CET. The proposed BCD is a generalized model to offer more capacity for measuring the difference among CBBAs. Additionally, other properties of the BCD are analyzed, including the non-negativeness, nondegeneracy, symmetry, and triangle inequality. Besides, a basis algorithm and its weighted extension for multi-attribute decision-making are designed based on the newly defined BCD. Finally, these decision-making algorithms are applied to cope with the medical diagnosis problem under the smart IoT environment to reveal their effectiveness.
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