After the addition of all the new features, it is the case that removal of no individual feature except predicate degrades the classification performance significantly, as there are some other features that provide complimentary information.
the best system is trained by first filtering the most likely nulls using the best NULL vs NON-NULL classifier trained using all the features whose argument identification F 1 score is marked in bold in table 4, and then training a ONE vs ALL classifier using the data remaining after performing the filtering and using the features that contribute positively to the classification task -ones whose accuracies are marked in bold in table 4.