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A new belief entropy measure in the weighted combination rule under DST with faulty diagnosis and real-life medical application

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Abstract

In Dempster-Shafer’s theory (also called DST or evidence theory or theory of belief function), conflict management is a well-known issue in information fusion. The paper presents a weighted evidence combination method consisting of a new approach to measuring information volume of mass function based on a proposed belief entropy and distance of evidence. The combination rule comprises four key points: firstly, the initial weight is obtained with the help of distance of evidence; secondly, information volume of mass function based on a new belief entropy is measured; thirdly final weight is obtained based on initial weight and information volume of mass function based on new belief entropy and lastly, final fusion is made with the help of Dempster combination rule. Also, it is shown here that the previously proposed approaches produce counterintuitive outcomes when the identical mass value is assigned to two different bodies of evidence (BOE). However, the approach proposed here can handle all such conflicting situations and significantly reduce the uncertainty of decisions. The efficacy of the proposed approach is shown with the help of a numerical experiment where the degree of belief is elevated to 0.9860 when five pieces of evidence conflict, a faulty diagnosis application where belief degree is elevated to 0.889 in comparison with existing works, and an application of real-world in the field of medical diagnosis where the belief degree raised to 0.9841 and the uncertainty of decision is lowered nearly to zero.

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We gratefully thank the reviewers for their constructive comments.

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  1. Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, 786004, India

    Palash Dutta & Sonom Shome

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  1. Palash Dutta
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Dutta, P., Shome, S. A new belief entropy measure in the weighted combination rule under DST with faulty diagnosis and real-life medical application. Int. J. Mach. Learn. & Cyber. 14, 1179–1203 (2023). https://doi.org/10.1007/s13042-022-01693-6

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