MULTI-LABEL CLASSIFICATION USING A LEARNED COMBINATION OF BASE CLASSIFIERS

Multi-label classification is performed by (i) applying a set of trained base classifiers to an object to generate base classifier label prediction sets comprising subsets of a set of labels; (ii) constructing a set of second level features including at least one second level feature defined by a predetermined combination of two or more of the base classifier label prediction sets; and (iii) applying a second level classifier to label the object with a set of one or more labels comprising a subset of the set of labels, labeling being based on the set of second level features. The multi-label classifier is trained by: (iv) applying operations (i) and (ii) to labeled training objects of a set of labeled training objects to generate training metadata comprising sets of second level features for the labeled training objects; and (v) training the second level classifier using the training metadata.