Chen Yutong, Tang Yongchuan
School of Computer and Information Science, Southwest University, Chongqing 400715, China.
School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China.
Entropy (Basel). 2021 Mar 28;23(4):402. doi: 10.3390/e23040402.
Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data. Therefore, this paper proposes a new data fusion method which combines the Deng entropy and the negation of basic probability assignment (BPA). In this method, the uncertain degree in the original BPA and the negation of BPA are considered simultaneously. The degree of uncertainty of BPA and negation of BPA is measured by the Deng entropy, and the two uncertain measurement results are integrated as the final uncertainty degree of the evidence. This new method can not only deal with the data fusion of conflicting evidence, but it can also obtain more uncertain information through the negation of BPA, which is of great help to improve the accuracy of information processing and to reduce the loss of information. We apply it to numerical examples and fault diagnosis experiments to verify the effectiveness and superiority of the method. In addition, some open issues existing in current work, such as the limitations of the Dempster-Shafer theory (DST) under the open world assumption and the necessary properties of uncertainty measurement methods, are also discussed in this paper.
邓普斯特-谢弗（DS）证据理论在不确定信息处理的各个领域中得到了广泛应用，但在处理冲突数据时可能会产生违反直觉的结果。因此，本文提出了一种新的数据融合方法，该方法结合了邓熵和基本概率赋值（BPA）的否定。在该方法中，同时考虑了原始BPA中的不确定度和BPA的否定。BPA的不确定度和BPA的否定通过邓熵来度量，并且将这两个不确定度测量结果整合为证据的最终不确定度。这种新方法不仅可以处理冲突证据的数据融合，还可以通过BPA的否定获得更多的不确定信息，这对于提高信息处理的准确性和减少信息损失有很大帮助。我们将其应用于数值示例和故障诊断实验，以验证该方法的有效性和优越性。此外，本文还讨论了当前工作中存在的一些开放性问题，例如开放世界假设下邓普斯特-谢弗理论（DST）的局限性以及不确定度测量方法的必要属性。
