Efficient Voting Prediction for Pairwise Multilabel Classification.
Eneldo Loza Mencía, Sang-Hyeun Park, Johannes Fürnkranz · 2009
Abstract. 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 n 2 to n + dn log n, wheren is the total number of labels and d is the average number of labels, which is typically quite small in real-world datasets. 1