[BibTeX] [RIS]
Efficient Voting Prediction for Pairwise Multilabel Classification
Type of publication: Inproceedings
Citation: jf:ESANN-09
Booktitle: Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN 2009, Bruges, Belgium)
Year: 2009
Month: April
Pages: 117--122
Publisher: d-side publications
ISBN: 2-930307-09-9
URL: http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2009-112.pdf
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, 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.
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Keywords:
Authors Loza Mencía, Eneldo
Park, Sang-Hyeun
Fürnkranz, Johannes
Topics