Algorithm 1 Working framework of ReliefF [35,36].

Input: for each training instance a vector of attribute values and the class value. Output: the vector W of estimations of the qualities of attributes. 1. set all weights W [A] := 0.0; 2. for i := 1 to n do begin 3.  randomly select an instance Ri; 4.  find k nearest hits Hj; 5.  for each class C ≠ class (Ri) do 6.      from class C find k nearest misses Mj(C); 7. for A := 1 to a do 8. $W[A]:=W[A]-{\displaystyle \sum _{j=1}^{l}diff(A,R_i,H_j)/(n\cdot k)}+{\displaystyle \sum _{C\ne class(R_i)}^l[\frac{P(C)}{1-P(class(R_i))}]{\displaystyle \sum _{j=1}^{l}diff(A,R_i,M_j(C))/(n\cdot k)}}$ 9. end;