Name
Abstract | ||
|---|---|---|
How to manage the uncertainty of the basic probability assignment accurately and efficiently is of significance and also an open issue. Plenty of functions have been established to cover the issue, especially Deng entropy recently. Deng entropy can deal with the more complex situation of the focal elements (propositions). However, Deng entropy has some limitations when the propositions are of the intersection. In this paper, a modified function is proposed by considering the scale of the frame of discernment and the influence of the intersection between statements on uncertainty. The proposed belief entropy provides a promising way to measure the uncertain information. Some numerical examples and an application in pattern recognition are used to show the efficiency and accuracy of the proposed belief entropy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2896286 | IEEE ACCESS |
Keywords | Field | DocType |
Entropy,Deng entropy,Shannnon entropy,Dempster-Shafer evidence theory,pattern recognition | Pattern recognition,Computer science,Measurement uncertainty,Artificial intelligence,Discernment | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huizi Cui | 1 | 4 | 1.39 |
| Qing Liu | 2 | 0 | 0.68 |
| Jianfeng Zhang | 3 | 4 | 1.85 |
| Bingyi Kang | 4 | 138 | 9.24 |