$A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification$

A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

Dempster–Shafer (D‐S) evidence theory is invaluable in the domain of multisource information fusion for handing uncertainty problems. However, there may be counter‐intuitive phenomenon when facing highly conflicting information. In this paper, a novel symmetric enhanced belief χ2 ${\chi }^{2}$ diver...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	International journal of intelligent systems 2022-10, Vol.37 (10), p.7968-7991
Hauptverfasser:	Zhang, Lang, Xiao, Fuyuan
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms belief function Chi-square test Data integration Dempster–Shafer theory evidence conflict Intelligent systems multisource information fusion Pattern classification symmetric enhanced belief χ2 divergence uncertainty
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!