Adaptative combination rule and proportional conflict redistribution rule for information fusion
M. C. Florea, J. Dezert, P. Valin, F. Smarandache, Anne-Laure Jousselme
Abstract
This paper presents two new promising rules of combination for the fusion of uncertain and potentially highly conflicting sources of evidences in the framework of the theory of belief functions in order to palliate the well-know limitations of Dempster's rule and to work beyond the limits of applicability of the Dempster-Shafer theory. We present both a new class of adaptive combination rules (ACR) and a new efficient Proportional Conflict Redistribution (PCR) rule allowing to deal with highly conflicting sources for static and dynamic fusion applications.
Create a lesson
Related papers
OSGR: Observation-Support Graph Reasoning for Earth Observation Question Answering
Dazhou Yu, Zerong Wang, Riyang Bao et al.
Bidirectional Centered Score Matching for Discrete Diffusion Distillation
Peng Tu, Baoyou Chen, Haoyuan Xia et al.
Harness-S: Learning from Sibling Harnesses for Generalizable Agent Training
Kangning Zhang, Wenxiang Jiao, Bowen Fang et al.
Reliability Stress Testing of RAG Retrieval under Query-Relevant Corpus Poisoning
Janet Sung, Kainan Zhou, Gangzhen Qian et al.
Neologism Inoculation Prompting
David Demitri Africa, Cameron Holmes
AceAction-5M: Hierarchical Goals for Generalist Action Learning Across 3D Game Worlds
Kailing Li, Ruoxuan Zhang, Zheyuan Zhou et al.