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. |
| Userfields: | opturl={"/publications/papers/ESANN09.pdf"}, |
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