Dempster–Shafer evidence theory plays a significant role in addressing uncertain information in various data fusion application systems. Recently, a new framework to model uncertain and partially reliable information on the basis of Dempster–Shafer evidence theory is put forward, called two-dimensional belief function (TDBF). A TDBF consists of two classical belief functions, \(T=(m_A,m_B)\), where \(m_B\) is a measure of reliability of \(m_A\). In this paper, an approach for determining TDBF is presented based on the improved similarity measure of fuzzy numbers. The improved similarity measure is more logical, flexible and can obviously improve the effectiveness in classification problem. Compared to the classical belief function, the TDBF can achieve better classification effective. The processes of the determine approach are expounded through a classification problem of Iris data. The validity of the determine approach is further illustrated by the classification of Wheat data.

Dempster–Shafer证据理论在解决各种数据融合应用系统中的不确定信息方面发挥着重要作用。最近，提出了一种基于Dempster-Shafer证据理论对不确定和部分可靠的信息进行建模的新框架，称为二维置信函数（TDBF）。甲TDBF包括两个经典信度函数，\（T =（M_A，M_B）\） ，其中\（M_B \）是中的可靠性的度量\（M_A \）。本文提出了一种基于改进的模糊数相似度度量的TDBF确定方法。改进的相似度度量更加逻辑，灵活，可以明显提高分类问题的有效性。与经典置信函数相比，TDBF可以实现更好的分类效果。确定方法的过程通过虹膜数据的分类问题进行了阐述。小麦数据分类进一步说明了确定方法的有效性。