Paper Info
Title
A Novel Complex Evidential Distance With Its Application In Pattern Recognition
Abstract
Complex evidence theory is an effective information fusion method, which can be used to fuse complex basic belief assignments (CBBAs) and obtain a reasonable result. In complex evidence theory, it is still an open issue for conflict management. In order to address this problem, this paper focuses on putting forward a new distance to measure conflict between CBBAs. The newly defined complex evidential distance takes into account not only the singletons, but also the subsets as well as their power sets. Therefore, it has a better performance to measure the conflict between the CBBAs. In addition, the proposed distance satisfies distance properties of nonnegativity, nondegeneracy, symmetry and the triangle inequality. In particular, when the CBBAs degenerate to classical BBAs, the proposed distance can also measure conflict well. Furthermore, a number of numerical examples are given to illustrate the above mentioned properties. Based on the newly devised distance measure, a basic pattern recognition algorithm is proposed. Subsequently, it is extended to a weighted scheme of pattern recognition algorithm. Finally, those two algorithms are applied to solve medical diagnosis to demonstrate their effectiveness.
Year
DOI
Venue
2021
10.1016/j.engappai.2021.104312
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Complex evidence theory, Complex basic belief assignments, Complex mass function, Distance measure, Conflict management, Pattern recognition, Decision-making
Journal
104
ISSN
Citations 
PageRank 
0952-1976
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Zhanhao Zhang100.34
Fuyuan Xiao220119.11