4.8 Article

Complex Network Modeling of Evidence Theory

Journal

IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 29, Issue 11, Pages 3470-3480

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2020.3023760

Keywords

Complex networks; Computational modeling; Sensors; Machine learning; Iris; Semisupervised learning; Computational complexity; Complex network; Dempster-Shafer evidence theory; evidential clustering; graph convolutional network (GCN); semisupervised learning

Funding

  1. National Natural Science Foundation of China [61973332]

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This article presents a novel model of evidence theory based on complex networks, addresses some typical issues of evidence theory, and introduces a new combination rule.
Because of the advantages of graphs in visualizing the relationship between individuals, complex networks have been widely used and greatly developed. In real-world applications of Dempster-Shafer evidence theory, there are usually thousands of sensors collecting information. It is easy to be overwhelmed by the mass of information and ignore the connections between them. The rise of the semisupervised learning method graph convolutional network makes it possible to address this issue. In this article, inspired by complex network, the basic probability assignment function, the base function of evidence theory, is modeled in a novel form of the network graph. Some typical issues of evidence theory, such as conflicting evidence, multiclass evidence clustering, and computational complexity for large-scale fusion are systematically addressed in the framework of the proposed network model. What's more, a new combination rule is presented from the point view of the graph. The empirical results of experiments on real data set demonstrate the potential and feasibility of complex networks in traditional evidence theory.

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