A fractal belief KL divergence for decision fusion. (May 2023)

• Record Type: Journal Article
• Authors: Zeng, Jie; Xiao, Fuyuan
• Is Part Of: Engineering applications of artificial intelligence. Volume 121(2023)
• Publication Date: 2023-05
• Subjects: Dempster–Shafer evidence theory -- Fractal belief KL divergence -- Conflict management -- Multi-source data fusion -- Classification; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.106027
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Dempster–Shafer (D–S) evidence theory is useful in the realm of multi-source data fusion. However, a counterintuitive result may be obtained when the belief probability assignments (BPAs) are highly conflicting. To overcome this flaw, in this paper a symmetric fractal-based belief Kullback–Leibler divergence ( F B D S K L ) is proposed. It is used to measure the divergence between BPAs, and is more capable than the existing belief divergence methods in measuring the conflict between two BPAs in numerical examples. Furthermore, the proposed F B D S K L is proved to have desirable properties including nonnegativity, nondegeneracy and symmetry. To apply F B D S K L divergence measure to practical problems, a novel F B D S K L -based multi-source data fusion ( F B D S K L -MSDF) algorithm is designed. Through comparisons with the well-known related methods, the proposed F B D S K L -MSDF algorithm is validated to be superior and more robust. Finally, the proposed F B D S K L -MSDF is applied to two real-world classification problems to verify its high practicability.