Efficient voting prediction for pairwise multilabel classification

The pairwise approach to multilabel classification reduces the problem to learning and aggregating preference predictions among the possible labels. A key problem is the need to query a quadratic number of preferences for making a prediction. To solve this problem, we extend the recently proposed QWeighted algorithm for efficient pairwise multiclass voting to the multilabel setting, and evaluate the adapted algorithm on several real-world datasets. We achieve an average-case reduction of classifier evaluations from n2 to n+dnlogn, where n is the total number of possible labels and d is the average number of labels per instance, which is typically quite small in real-world datasets.