English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Dempster-Shafer evidence theory is an important methodology for multi-source information fusion depending on its advantages in dealing with uncertain information. The classical fusion rule proposed by Dempster, however, fails to handle evidence with high conflict in some cases, where the counter-intuitive results may be obtained. To address this issue, great efforts have been made by scholars from different perspectives, such as improving the combination rule or amending sources of evidence. Motivated by the idea of the technique for order preference by similarity to ideal solution method, in this paper, the evidential reliability indicator is defined to measure the quality of a mass function (which can also be seen as its reliability) based on belief entropy. Accordingly, the novel fusion rule for multi-source evidence bodies is presented whose effectiveness has been demonstrated by some numerical examples and applications in classification by comparing with several classical approaches. The novelty aspects and advantages of this research rest with the development of an evidential reliability indicator that can 1) measure the credibility of evidence body; 2) be considered as the basis of the new combination rule; and 3) build the classification algorithm based on the proposed fusion rule.

An Evidential Reliability Indicator-Based Fusion Rule for Dempster-Shafer Theory and its Applications in Classification