Algorithm 2 Generating decision tree.

Input:D: the training set; C: the attribute set. Output: A decision tree  Function TreeGenerate $(D,C)$ 1: Create a node N 2: if tuples in D belong to only one class C then 3:  label N as a leaf node with class C; return 4: end if 5: if C is empty OR the samples of D are of the same class then 6:  set label N as the leaf node with the most common class in D; return 7: end if 8: Find the best splitting criterion $c_{*}$ from C 9: for each $c_{*}$ do 10:  add a branch below N, corresponding to $c_{*}=c_{*}^v$ 11:  $D_v$ is the subset of D with $c_{*}=c_{*}^v$ 12:   if $D_v$ is empty then 13:   label the branch node as the leaf node with the most common class in D; return 14:   else 15:   set TreeGenerate $(D_v,C\setminus \left\{c_{*}\right\})$ as the branch node 16:   end if 17: end for