The basic idea of our active learning approach is to use two complementary classifiers for collectively selecting most informative MI samples: one to adopt a certainty measurement for selecting most possible MI samples and the other to adopt an uncertainty measurement for selecting most uncertain MI samples from the most possible MI samples returned from the first classifier.
the two classifiers are trained with two disjoint feature subspaces to guarantee their complementariness.