Name
Title | ||
|---|---|---|
A Novel Weighted Evidence Combination Rule Based On Improved Entropy Function With A Diagnosis Application |
Abstract | ||
|---|---|---|
Managing conflict in Dempster-Shafer theory is a popular topic. In this article, we propose a novel weighted evidence combination rule based on improved entropy function. This newly proposed approach can be mainly divided into two steps. First, the initial weight will be determined on the basis of the distance of evidence. Then, this initial weight will be modified using improved entropy function. This new method converges faster when handling high conflicting evidences and greatly reduces uncertainty of decisions, which can be demonstrated by a numerical example where the belief degree is raised up to 0.9939 when five evidences are in conflict, an application in faulty diagnosis where belief degree is increased hugely from 0.8899 to 0.9416 when compared with our previous works, and a real-life medical diagnosis application where the uncertainty of decision is reduced to nearly 0 and the belief degree is raised up to 0.9989. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1177/1550147718823990 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
Keywords | Field | DocType |
Dempster-Shafer evidence theory, information fusion, entropy function, faulty diagnosis, medical diagnosis | Rule-based system,Computer science,Binary entropy function,Artificial intelligence,Information fusion,Medical diagnosis,Distributed computing | Journal |
Volume | Issue | ISSN |
15 | 1 | 1550-1477 |
Citations | PageRank | References |
1 | 0.35 | 32 |
Authors | ||
3 | ||