We consider the task of multilabel classification, where each instance may belong to
multiple labels simultaneously. We propose a new method, called multilabel super
learner (MLSL), that is built upon the problem transformation approach using the
one-vs-all binary relevance method. MLSL is an ensemble model that predicts
multilabel responses by integrating the strength of multiple base classifiers, and
therefore it is likely to outperform each base learner. The weights in the
ensemble classifier are determined by optimization of a loss function via
-fold
cross-validation. Several loss functions are considered and evaluated numerically. The
performance of various realizations of MLSL is compared to existing problem
transformation algorithms using three real data sets, spanning applications in
biology, music, and image labeling. The numerical results suggest that MLSL
outperforms existing methods most of the time evaluated by the commonly used
performance metrics in multilabel classification.
Keywords
binary relevance, heterogeneous ensemble, multilabel
classification, stacking, super learner
