Name
Abstract | ||
|---|---|---|
When Dempster's rule is used to implement a combination of evidence, all sources are considered equally reliable. However, in many real applications, all the sources of evidence may not have the same reliability. To resolve this problem, a number of methods for discounting unreliable sources of evidence have been proposed in which the estimation of the discounting (weighting) factors is crucial, especially when prior knowledge is unavailable. In this paper, we propose a new degree of disagreement through which discounting factors can be generated for discounting combinations of unreliable evidence. The new degree of disagreement is established using distance of evidence. It can be experimentally verified that our degree of disagreement describes the disagreements or differences among bodies of evidence well and that it can be effectively used in discounting combinations of unreliable evidence. © 2013 Elsevier Inc. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.ijar.2013.04.002 | International Journal of Approximate Reasoning |
Keywords | Field | DocType |
Evidence theory,Belief function,Distance of evidence,Discounting | Econometrics,Weighting,Discounting,Proof theory,Artificial intelligence,Dempster–Shafer theory,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
54 | 8 | 0888-613X |
Citations | PageRank | References |
24 | 0.73 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Yang | 1 | 47 | 4.35 |
| Deqiang Han | 2 | 218 | 22.90 |
| Chongzhao Han | 3 | 446 | 71.68 |