.png){kind=icon}
返回
Complex evidential reasoning-based multi-source information fusion for pattern classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2026.132891.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Complex evidence theory (CET) extends the description space of the Dempster-Shafer (D-S) evidence theory from the real domain to the complex domain, which has greatly improved its decision-making ability. In the decision-making process, the weight and reliability of the evidence sources are two indispensable core elements. Their synergistic effect forms an adaptive mechanism that serves as the key to eliminating interference from untrustworthy information, directly determining the scientific rigor and credibility of decisions. However, CET fails to account for these two critical factors, and this deficiency severely limits its ability to handle complex multi-source information scenarios while undermining the persuasiveness of its decision outcomes. Therefore, this article proposes a reasoning model called complex evidence reasoning (CER) based on CET, that integrates multiple pieces of evidence by considering their weights and reliability. We propose a method for generating complex mass function (CMF) with evidence weights, define a complex weighted belief distribution with reliability (CWRBD), and propose a new combination rule called the multi-angle collaborative (MAC) combination rule. Numerical examples are provided to verify the effectiveness of the proposed CER. Moreover, the proposed CER is applied to classification problems, and the results show that the CER-based classification algorithm is superior to other methods in terms of accuracy and stability.
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
暂无机构信息
引用论文
.png){kind=icon}
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
PREAI
分享收藏
Representation and combination of uncertainty with belief functions and possibility measures不确定性与信念函数和可能性度量的表示和组合
PREAI
分享收藏
查看更多内容