EconPapers    
Economics at your fingertips  
 

Quantification Classification Algorithm of Multiple Sources of Evidence

Jian-Ping Yang (), Hong-Zhong Huang (), Yu Liu () and Yan-Feng Li ()
Additional contact information
Jian-Ping Yang: School of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, P. R. China;
Hong-Zhong Huang: School of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, P. R. China
Yu Liu: School of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, P. R. China
Yan-Feng Li: School of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2015, vol. 14, issue 05, 1017-1034

Abstract: Although Dempster–Shafer (D–S) evidence theory and its reasoning mechanism can deal with imprecise and uncertain information by combining cumulative evidences for changing prior opinions of new evidences, there is a deficiency in applying classical D–S evidence theory combination rule when conflict evidence appear — conflict evidence causes counter-intuitive results. To address this issue, alternative combination rules have been proposed for resolving the appeared conflicts of evidence. An underlying assumption is that conflict evidences exist, which, however, is not always true. Moreover, it has been verified that conflict factors may not be accurate to characterize the degree of conflict. Instead, the Jousselme distance has been regarded as a quantification criterion for the degree of conflict because of its promising properties. To avoid the counter-intuitive results, multiple sources of evidence should be classified first. This paper proposes a novel algorithm to quantify the classification of multiple sources of evidence based on a core vector method, and the algorithm is further verified by two examples. This study also explores the relationship between complementary information and conflicting evidence and discusses the stochastic interpretation of basic probability assignment functions.

Keywords: Dempster–Shafer evidence theory; evidence distance; evidence conflict; quantification classification (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622014500242
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:14:y:2015:i:05:n:s0219622014500242

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622014500242

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().

 
Page updated 2025-03-20
Handle: RePEc:wsi:ijitdm:v:14:y:2015:i:05:n:s0219622014500242