Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster–Shafer Theory With Applications in Pattern Classification
Xingyu Liu, Linlin Fan, Xuekai Wei, Jielu Yan, Jun Luo, Huayan Pu, Weijia Jia, Mingliang Zhou · IEEE Transactions on Neural Networks and Learning Systems · 2026
In the construction of complex decision-making systems, which often involve uncertainties from multiple sources of information, effectively expressing the uncertainty of information remains an unresolved issue. Therefore, on the basis of Dempster-Shafer theory (DST), we define the negation of basic belief assignment (NBBA) via the difference measure of focal elements, investigate this problem from a negation perspective, and apply the NBBA in decision-making. First, we propose a kernel entropy adaptive focal element generation method (KEAFG) for constructing expressive basic belief assignments (BBAs). On this basis, we effectively construct NBBAs. Furthermore, to provide a theoretical foundation for decision-making based on the NBBA, we analyze and discuss the theoretical properties of the NBBA. We subsequently propose a multisource information fusion classification method based on the NBBA (MSIF-NBBA). This method involves joint decision-making on the BBAs obtained by the KEAFG and the NBBAs obtained by the NBBA to obtain classification results. MSIF-NBBA has been validated on several datasets, including CIFAR-10, MNIST, Fashion-MNIST, Iris, and Heart. Experimental results on multiple datasets demonstrate that the proposed method significantly outperforms state-of-the-art methods. The proposed method effectively reduces uncertainty in multisource information fusion and improves decision-making. The code is available at https://github.com/LeonidMs-L/MSIF-NBBA.