TY - CONF ID - jf:IJCNN-08 T1 - Pairwise Learning of Multilabel Classifications with Perceptrons A1 - Loza Mencía, Eneldo A1 - Fürnkranz, Johannes TI - Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IJCNN-08) Y1 - 2008 SP - 2900 EP - 2907 T2 - IEEE AD - Hong Kong SN - 978-1-4244-1821-3 UR - /publications/papers/loza08MLPP.pdf M2 - doi: 10.1109/IJCNN.2008.4634206 N2 - Multiclass multilabel perceptrons (MMP) have been proposed as an efficient incremental training algorithm for addressing a multilabel prediction task with a team of perceptrons. The key idea is to train one binary classifier per label, as is typically done for addressing multilabel problems, but to make the training signal dependent on the performance of the whole ensemble. In this paper, we propose an alternative technique that is based on a pairwise approach, i.e., we incrementally train a perceptron for each pair of classes. Our evaluation on four multilabel datasets shows that the multilabel pairwise perceptron (MLPP) algorithm yields substantial improvements over MMP in terms of ranking quality and overfitting resistance, while maintaining its efficiency. Despite the quadratic increase in the number of perceptrons that have to be trained, the increase in computational complexity is bounded by the average number of labels per training example. ER -