Reducing Algorithm Complexity for Computing an Aggregate Uncertainty Measure

In the theory of evidence, two kinds of uncertainty coexist, nonspecificity and discord. An aggregate uncertainty (AU) measure has been defined to include these two kinds of uncertainty, in an aggregate fashion. Meyerowitz et al. proposed an algorithm for calculating AU and validated its practical u...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Jg. 37; H. 5; S. 669 - 679
Hauptverfasser: Chunsheng Liu, Grenier, D., Jousselme, A-L., Bosse, E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2007
Schlagworte:
Aggregate uncertainty (AU)
Aggregates
Algorithms
Collaborative work
Complexity
Computation
Computational complexity
Councils
Cybernetics
Decision support systems
Dempster-Shafer (D-S) theory
Entropy
Gold
Human
Mathematical analysis
Measurement uncertainty
Research and development
Set theory
Uncertainty
ISSN:1083-4427
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In the theory of evidence, two kinds of uncertainty coexist, nonspecificity and discord. An aggregate uncertainty (AU) measure has been defined to include these two kinds of uncertainty, in an aggregate fashion. Meyerowitz et al. proposed an algorithm for calculating AU and validated its practical usage. Although this algorithm was proven to be absolutely correct by Klir and Wierman, in some cases, it remains too complex. In fact, when the cardinality of the frame of discernment is very large, it can be impossible to calculate AU. Therefore, based on Klir's and Harmanec's seminal work, we give some justifications for restricting the computation of AU(Bel) to the core of the corresponding belief function, and we also propose an algorithm to calculate AU(Bel), the F-algorithm, which reduces the computational complexity of the original algorithm of Meyerowitz et al. We prove that this algorithm gives the same results as Meyerowitz's algorithm, and we outline conditions under which it reduces the computational complexity significantly. Moreover, we illustrate the use of the F-algorithm in computing AU in a practical scenario of target identification.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1083-4427
DOI:10.1109/TSMCA.2007.893457