[BibTeX] [RIS]
Pairwise Learning of Multilabel Classifications with Perceptrons
Type of publication: Inproceedings
Citation: jf:IJCNN-08
Booktitle: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IJCNN-08)
Year: 2008
Pages: 2900--2907
Organization: IEEE
Address: Hong Kong
ISBN: 978-1-4244-1821-3
URL: http://www.ke.tu-darmstadt.de/publications/papers/loza08MLPP.pdf
DOI: 10.1109/IJCNN.2008.4634206
Abstract: 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.
Authors Loza Mencía, Eneldo
Fürnkranz, Johannes