In Dempster–Shafer evidence theory, the basic probability assignment (BPA) can effectively represent and process uncertain information. How to transform the BPA of uncertain information into a decision probability remains a problem to be solved. In the light of this issue, we develop a novel decision probability transformation method to realize the transition from the belief decision to the probability decision in the framework of Dempster–Shafer evidence theory. The newly proposed method considers the transformation of BPA with multi-subset focal elements from the perspective of the belief interval, and applies the continuous interval argument ordered weighted average operator to quantify the data information contained in the belief interval for each singleton. Afterward, we present an approach to calculate the support degree of the singleton based on quantitative data information. According to the support degree of the singleton, the BPA of multi-subset focal elements is allocated reasonably. Furthermore, we introduce the concepts of probabilistic information content in this paper, which is utilized to evaluate the performance of the decision probability transformation method. Eventually, a few numerical examples and a practical application are given to demonstrate the rationality and accuracy of our proposed method.

在Dempster–Shafer证据理论中，基本概率分配（BPA）可以有效地表示和处理不确定信息。如何将不确定信息的BPA转换为决策概率仍然是有待解决的问题。针对这一问题，我们开发了一种新颖的决策概率转换方法，以实现从信念决策到概率决策在Dempster-Shafer证据理论框架下的过渡。新提出的方法从置信区间的角度考虑了具有多子集焦点元素的BPA的转换，并应用连续区间参数有序加权平均算子来量化每个单例置信区间中包含的数据信息。之后，我们提出了一种基于定量数据信息来计算单身人士的支持度的方法。根据单例的支持程度，合理分配多子集焦点元素的BPA。此外，我们介绍了概率信息内容的概念，用于评估决策概率转换方法的性能。最后，通过数值算例和实际应用证明了该方法的合理性和准确性。用来评估决策概率转换方法的性能。最后，通过数值算例和实际应用证明了该方法的合理性和准确性。用来评估决策概率转换方法的性能。最后，通过数值算例和实际应用证明了该方法的合理性和准确性。