航空学报2026，Vol.47Issue(12)：295-311,17.DOI:10.7527/S1000-6893.2025.32927
利用学习机制的多方法融合端到端证据建模
Learning-based BBA modeling approach with multi-method fusion
摘要
Abstract
Dempster-Shafer evidence Theory(DST)is a theoretical framework for uncertainty modeling and reason-ing,in which modeling the Basic Belief Assignment(BBA)constitutes a crucial and challenging part.The prevailing BBA determination methods have their own pros and cons,and the joint use of them is expected to provide a better BBA.However,explicitly using several BBA determination methods and combining the BBAs through a specific fusion rule is inefficient.To address this issue,we propose a learning-based BBA modeling approach with multi-method fu-sion.A deep network is trained which learns the mapping from the training samples to the comprehensive BBAs ob-tained by jointly using the prevailing BBA modeling methods as the generalized training labels.Experimental results on remote sensing image datasets and UCI datasets demonstrate that the proposed method outperforms the individual BBA modeling methods in terms of classification performance.关键词
基本信度分配/证据理论/深度学习/模式分类/数据驱动Key words
Basic Belief Assignment(BBA)/evidence theory/deep learning/pattern classification/data-driven分类
航空航天引用本文复制引用
李思远,韩德强,Jean DEZERT,杨艺..利用学习机制的多方法融合端到端证据建模[J].航空学报,2026,47(12):295-311,17.基金项目
国家自然科学基金(62473304,U22A2045) National Natural Science Foundation of China(62473304,U22A2045) （62473304,U22A2045）
