Loza Mencía, Eneldo ; Park, Sang-Hyeun ; Fürnkranz, Johannes (2010)
Efficient Voting Prediction for Pairwise Multilabel Classification.
In: Neurocomputing, 73 (7-9)
doi: 10.1016/j.neucom.2009.11.024
Artikel, 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 + n d log n, where n is the total number of possible labels and d is the average number of labels per instance, which is typically quite small in real-world datasets.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2010 |
| 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: | 2010 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Neurocomputing |
| Jahrgang/Volume einer Zeitschrift: | 73 |
| (Heft-)Nummer: | 7-9 |
| DOI: | 10.1016/j.neucom.2009.11.024 |
| 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 + n d log n, where n is the total number of possible labels and d is the average number of labels per instance, which is typically quite small in real-world datasets. |
| Freie Schlagworte: | efficient classification, learning by pairwise comparison, multilabel classification, voting aggregation |
| Zusätzliche Informationen: | Advances in Computational Intelligence and Learning - 17th European Symposium on Artificial Neural Networks 2009, 17th European Symposium on Artificial Neural Networks 2009 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Knowledge Engineering |
| Hinterlegungsdatum: | 24 Jun 2011 14:24 |
| Letzte Änderung: | 26 Aug 2018 21:26 |
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