Paper Info
Title
Complex belief interval-based distance measure with its application in pattern recognition
Abstract
The complex evidence theory is an effective methodology for multiattribute decision-making. Since difference measure between multiattribute plays an important role for conflict management in the process of multiattribute decision-making, how to measure discrepancy between complex basic belief assignments (CBBAs) in complex evidence theory is still an open issue. In this context, a new distance measurement (complex belief distance-CBD) is proposed in this paper by taking advantages of complex belief function and complex plausibility function, called complex belief interval-based distance. In addition, we compare the proposed CBD with the related work to illustrate its superiority. Next, based on CBD, we devise a novel multiattribute decision-making algorithm for pattern recognition. Finally, we apply the method to problems of medical diagnosis to verify the effectiveness of the proposed method.
Year
DOI
Venue
2022
10.1002/int.22863
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Keywords
DocType
Volume
belief interval-based distance measure, complex basic belief assignments, complex evidence theory, conflict managament, information fusion, pattern recognition
Journal
37
Issue
ISSN
Citations 
10
0884-8173
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Zhanhao Zhang100.34
Fuyuan Xiao220119.11