Ni Shuang, Lei Yan, Tang Yongchuan
School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China.
Entropy (Basel). 2020 Jul 22;22(8):801. doi: 10.3390/e22080801.
Due to the nature of the Dempster combination rule, it may produce results contrary to intuition. Therefore, an improved method for conflict evidence fusion is proposed. In this paper, the belief entropy in D-S theory is used to measure the uncertainty in each evidence. First, the initial belief degree is constructed by using an improved base belief function. Then, the information volume of each evidence group is obtained through calculating the belief entropy which can modify the belief degree to get the final evidence that is more reasonable. Using the Dempster combination rule can get the final result after evidence modification, which is helpful to solve the conflict data fusion problems. The rationality and validity of the proposed method are verified by numerical examples and applications of the proposed method in a classification data set.
由于Dempster组合规则的性质，它可能产生与直觉相反的结果。因此，提出了一种改进的冲突证据融合方法。本文利用D-S理论中的信度熵来度量各证据中的不确定性。首先，通过改进的基本信度函数构建初始信度度。然后，通过计算信度熵得到各证据组的信息量，信度熵可以修正信度度以获得更合理的最终证据。使用Dempster组合规则可以在证据修正后得到最终结果，这有助于解决冲突数据融合问题。通过数值算例以及该方法在分类数据集中的应用，验证了所提方法的合理性和有效性。
