Input: train_data a labeled training set, test_data a labeled testing set |
Output: PF a list of promising features |
(1) an error range, the botnet traffic detection rate, |
the current botnet traffic detection rate |
(2) initialization , |
(3) = Randonforest (all features) |
(4) while do |
(5) |
(6) calculate RF scores of importance |
(7) rank the RF scores |
(8) delete the feature with the smallest importance from train_data and test_data |
(9) = randomforest (remaining_features) |
(10) end while |