Loza Mencía, Eneldo ; Park, Sang-Hyeun ; Fürnkranz, Johannes (2009)
Efficient Voting Prediction for Pairwise Multilabel Classification.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2009 |
| Autor(en): | Loza Mencía, Eneldo ; Park, Sang-Hyeun ; Fürnkranz, Johannes |
| Art des Eintrags: | Bibliographie |
| Titel: | Efficient Voting Prediction for Pairwise Multilabel Classification |
| Sprache: | Englisch |
| Publikationsjahr: | 2009 |
| Verlag: | d-side publications |
| Buchtitel: | Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN-09) |
| URL / URN: | http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2009-... |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Knowledge Engineering |
| Hinterlegungsdatum: | 24 Jun 2011 14:50 |
| Letzte Änderung: | 26 Aug 2018 21:26 |
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