An Evidence Combination Method based on DBSCAN Clustering

Dempster-Shafer (D-S) evidence theory is a key technology for integrating uncertain information from multiple sources. However, the combination rules can be paradoxical when the evidence seriously conflict with each other. In the paper, we propose a novel combination algorithm based on unsupervised...

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Bibliographic Details
Published inComputers, materials & continua Vol. 57; no. 2; pp. 269 - 281
Main Authors Yang, Kehua, Tan, Tian, Zhang, Wei
Format Journal Article
LanguageEnglish
Published Henderson Tech Science Press 2018
Subjects
Algorithms
Clustering
Computer simulation
Data integration
Density
Multisensor fusion
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Summary:Dempster-Shafer (D-S) evidence theory is a key technology for integrating uncertain information from multiple sources. However, the combination rules can be paradoxical when the evidence seriously conflict with each other. In the paper, we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) density clustering. In the proposed mechanism, firstly, the original evidence sets are preprocessed by DBSCAN density clustering, and a successfully focal element similarity criteria is used to mine the potential information between the evidence, and make a correct measure of the conflict evidence. Then, two different discount factors are adopted to revise the original evidence sets, based on the result of DBSCAN density clustering. Finally, we conduct the information fusion for the revised evidence sets by D-S combination rules. Simulation results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence, with better accuracy, stability and convergence speed.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2018.03696