Procedure 2.

Procedure: ImRMR

input:

X: universal set of all available features.

Xs: feature subset that has been selected.

Y: real label.

L: predicted labels of the previously built classifiers.

m: the number of additional features to be selected.

output: Selected features F, a subset X − Xs.

begin

Calculated the conditional mutual information, DCMI(Xi, Xj) between every pair of features Xi, Xj ε X ∪ L − Xs.

forr = m + 2 tom + c+ 1 do

Use DCMI(Xi, Xj) as distances to perform hierarchical clustering on X ∪ L − Xs until r clusters are obtained.

Eliminate every cluster C than L ∩ C ≠ ø.

Select the feature with the highest mutual information from each remaining cluster. Remove the least relevant feature from all selected features.

Let the selected features be F.

if |F| = mthen exit loop

end

Output F and stop.

end