基于聚类赋权的冲突证据组合方法

doi:10.11959/j.issn.1000-436x.2023029

Abstract

Abstract:

Aiming at the failure of the combination of highly conflicting evidence, the combination of the relationship between evidences and the characteristics of evidence itself was considered, and a method of conflicting evidence combination based on clustering weighting was proposed.Firstly, the similarity coefficient was constructed to cluster the evidence, and the inter-class weight was determined by class capacity to reflect the differences among classes.Then the information entropy was used to obtain the intra-class weight of each evidence.The modified weight of the evidence was obtained by combining the inter-class weight and intra-class weight.Finally, the conflict evidence was modified according to the weight, and the combination rule of evidence theory was used to achieve the final synthesis of the modified evidence.The results of the example show that the proposed method can reasonably carry out evidence correction, and the combined results have better focusing effect than other methods.

Key words: evidence theory, conflict, combination rule, clustering weighting

CLC Number:

TP391

Yu DONG, Youpeng ZHANG. Conflict evidence combination method based on clustering weighting[J]. Journal on Communications, 2023, 44(3): 157-163.

Figures/Tables 3

References 24

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组合方法	m₁, m₂	m₁, m₂, m₃	m₁, m₂, m₃, m₄	m₁, m₂, m₃, m₄, m₅	m₁, m₂, m₃, m₄, m₅, m₆
	m(θ₁)=0	m(θ₁)=0	m(θ₁)=0	m(θ₁)=0	m(θ₁)=0
证据理论组合规则	m(θ₂)=0.773	m(θ₂)=0.745	m(θ₂)=0.493	m(θ₂)=0.378	m(θ₂)=0.603
	m(θ₃)=0.227	m(θ₃)=0.255	m(θ₃)=0.507	m(θ₃)=0.622	m(θ₃)=0.397
	m(θ₁)=0.237	m(θ₁)=0.247	m(θ₁)=0.533	m(θ₁)=0.570	m(θ₁)=0.819
文献[12]方法	m(θ₂)=0.658	m(θ₂)=0.632	m(θ₂)=0.355	m(θ₂)=0.281	m(θ₂)=0.140
	m(θ₃)=0.105	m(θ₃)=0.121	m(θ₃)=0.112	m(θ₃)=0.148	m(θ₃)=0.041
	m(θ₁)=0.237	m(θ₁)=0.313	m(θ₁)=0.698	m(θ₁)=0.705	m(θ₁)=0.910
文献[13]方法	m(θ₂)=0.658	m(θ₂)=0.539	m(θ₂)=0.179	m(θ₂)=0.135	m(θ₂)=0.052
	m(θ₃)=0.105	m(θ₃)=0.148	m(θ₃)=0.123	m(θ₃)=0.160	m(θ₃)=0.038
	m(θ₁)=0.022	m(θ₁)=0.008	m(θ₁)=0.016	m(θ₁)=0.009	m(θ₁)=0.025
文献[14]方法（α=2）	m(θ₂)=0.927	m(θ₂)=0.971	m(θ₂)=0.970	m(θ₂)=0.981	m(θ₂)=0.971
	m(θ₃)=0.051	m(θ₃)=0.021	m(θ₃)=0.014	m(θ₃)=0.010	m(θ₃)=0.004
	m(θ₁)=0.082	m(θ₁)=0.115	m(θ₁)=0.496	m(θ₁)=0.514	m(θ₁)=0.820
文献[15]方法	m(θ₂)=0.846	m(θ₂)=0.804	m(θ₂)=0.401	m(θ₂)=0.358	m(θ₂)=0.149
	m(θ₃)=0.072	m(θ₃)=0.080	m(θ₃)=0.103	m(θ₃)=0.128	m(θ₃)=0.031
	m(θ₁)=0.467	m(θ₁)=0.569	m(θ₁)=0.829	m(θ₁)=0.786	m(θ₁)=0.910
文献[16]方法	m(θ₂)=0.399	m(θ₂)=0.232	m(θ₂)=0.051	m(θ₂)=0.041	m(θ₂)=0.048
	m(θ₃)=0.134	m(θ₃)=0.199	m(θ₃)=0.120	m(θ₃)=0.173	m(θ₃)=0.052
	m(θ₁)=0.024	m(θ₁)=0.860	m(θ₁)=0.929	m(θ₁)=0.962	m(θ₁)=0.968
本文方法	m(θ₂)=0.924	m(θ₂)=0.036	m(θ₂)=0.016	m(θ₂)=0.028	m(θ₂)=0.014
	m(θ₃)=0.052	m(θ₃)=0.104	m(θ₃)=0.055	m(θ₃)=0.030	m(θ₃)=0.018

Conflict evidence combination method based on clustering weighting

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