Algorithm 2 Splitting Rules based on Deng Entropy

Require: A root node $\mathbf{X}={\left\{{\mathbf{x}}_i\right\}}_{i=1}^N$, where $x_i$ is the ith instance with n condition attributes ${\left\{A_k\right\}}_{k=1}^n$ and one decision attribute D; the stopping criterion: Until all conditional attributes are used up. Ensure: An Evidential Decision Tree.  if the samples in X belong to some class then   Mark X as a leaf node and assign the class as its label.   return.  end if  for each attribute $A_k,k=1,2,\cdots ,n$ in X do   Computer Deng Enropy $E_d\left(A_k\right)$ according to Equation (10)   $E_d\left(A_k\right)=-{\displaystyle \frac{{\sum }_{i=1}^N{\sum }_{w\subseteq \mathsf{\Theta }}{c}_i^k\left(w\right){\log }_2\frac{c_i^k\left(w\right)}{2^{\left|w\right|}-1}}{N}}$$c_i^k$ represent a BBA for the instance i of attribute $A_k$   $E_d\left(A_{k^{*}}\right)=argmi{n}_{A_k}{E}_d\left(A_k\right)$. The smaller the entropy value, the better the subsequent division.  end for  Get the best attribute $A_{k^{*}}$ and the splitting point $c^{k^{*}}$.