Algorithm 1 Parameter estimation of GMMs.

Input: evidential training set $T_{pl}=(\mathit{x},p{l}_y)$, iteration stop threshold $ϵ$ Output: estimated normal distribution parameter matrix $\left({\mu }_k^d,{\sigma }_k^d\right),d=1,\dots ,D,k=1,\dots ,K$ 1:for each attribute $A^d$ do 2: initialize parameters as ${{\theta }^d}^{\left(0\right)}=\left({{\mu }_1^d}^{\left(0\right)},\dots ,{{\mu }_K^d}^{\left(0\right)},{{\sigma }_1^d}^{\left(0\right)},\dots ,{{\sigma }_K^d}^{\left(0\right)},{{\pi }_1^d}^{\left(0\right)},\dots ,{{\pi }_K^d}^{\left(0\right)}\right)$; 3: $q=0;$ Initialize loop variable. 4: for q do 5:   update the estimation of parameters ${{\theta }^d}^{(q+1)}=\left({{\mu }_1^d}^{(q+1)},\dots ,{{\mu }_K^d}^{(q+1)},{{\sigma }_1^d}^{(q+1)},\dots ,{{\sigma }_K^d}^{(q+1)},{{\pi }_1^d}^{(q+1)},\dots ,{{\pi }_K^d}^{(q+1)}\right)$ by Equations (28) and (29). 6:   if $L\left({{\theta }^d}^{\left(q+1\right)}\right)-L\left({{\theta }^d}^{\left(q\right)}\right)⩽ϵ$ then 7:    break; {End the loop if evidential likelihood increment is less than threshold.} 8:   end if 9:   $q=q+1$; 10:  end for 11:  adopt $\left({{\mu }_k^d}^{(q+1)},{{\sigma }_k^d}^{(q+1)}\right),k=1,\dots ,K$ as estimated normal distribution parameters under attribute $A^d$; 12:end for