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Efficient Voting Prediction for Pairwise Multilabel Classification

Loza Mencía, Eneldo and Park, Sang-Hyeun and Fürnkranz, Johannes (2009):
Efficient Voting Prediction for Pairwise Multilabel Classification.
In: Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN-09), d-side publications, pp. 117-122, ISBN 2-930307-09-9,
[Online-Edition: http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2009-...],
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2009
Creators: Loza Mencía, Eneldo and Park, Sang-Hyeun and Fürnkranz, Johannes
Title: Efficient Voting Prediction for Pairwise Multilabel Classification
Language: English
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.

Title of Book: Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN-09)
Publisher: d-side publications
ISBN: 2-930307-09-9
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Knowl­edge En­gi­neer­ing
Date Deposited: 24 Jun 2011 14:50
Official URL: http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2009-...
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