TY - CONF ID - jf:ESANN-09 T1 - Efficient Voting Prediction for Pairwise Multilabel Classification A1 - Loza Mencía, Eneldo A1 - Park, Sang-Hyeun A1 - Fürnkranz, Johannes TI - Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN 2009, Bruges, Belgium) Y1 - 2009 SP - 117 EP - 122 PB - d-side publications SN - 2-930307-09-9 UR - http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2009-112.pdf N2 - 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, where n is the total number of labels and d is the average number of labels, which is typically quite small in real-world datasets. M1 - opturl={"http://www.ke.tu-darmstadt.de/publications/papers/ESANN09.pdf"} ER -