TY - JOUR ID - jf:Neurocomputing T1 - Efficient Voting Prediction for Pairwise Multilabel Classification A1 - Loza Mencía, Eneldo A1 - Park, Sang-Hyeun A1 - Fürnkranz, Johannes JA - Neurocomputing Y1 - 2010 VL - 73 IS - 7-9 SP - 1164 EP - 1176 SN - 0925-2312 UR - http://www.ke.tu-darmstadt.de/publications/papers/neucom10.pdf M2 - doi: 10.1016/j.neucom.2009.11.024 KW - efficient classification KW - learning by pairwise comparison KW - multilabel classification KW - voting aggregation 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 n d log n, 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. M1 - note2={Volume: Advances in Computational Intelligence and Learning - 17th European Symposium on Artificial Neural Networks 2009 M1 - 17th European Symposium on Artificial Neural Networks 2009} ER -