Algorithms A1 Calculating objective weights by the entropy weight method
Input: For totally N samples and M corresponding features, the j-th feature value of the i-th sample $x_{ij}\left(i=1,2,\dots ,N;j=1,2,\dots M\right)$;
Process:
1. $X_{ij}=\frac{x_{ij}-\min \left\{x_{1j},\cdots ,x_{nj}\right\}}{\max \left\{x_{1j},\cdots x_{nj}\right\}-\min \left\{x_{1j},\cdots ,x_{nj}\right\}}$	% Normalization of positive influence feature
2. $X_{ij}=\frac{\max \left\{x_{1j},\cdots ,x_{nj}\right\}-x_{ij}}{\max \left\{x_{1j},\cdots x_{nj}\right\}-\min \left\{x_{1j},\cdots ,x_{nj}\right\}}$	% Normalization of negative influence feature
3. $k=\frac{1}{\ln \left(n\right)}>0$, $e_j=-k{\sum }_{i=1}^n{p}_{ij}\ln \left(p_{ij}\right),j=1,2,\cdots ,m$
	% Entropy value of the j-th feature
4. $d_j=1-e_j,j=1,2,\cdots ,m$	% Information entropy redundancy of the j-th feature
end Output: Subjective weight of the j-th feature