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Efficient Voting Prediction for Pairwise Multilabel Classification

Loza Mencía, Eneldo and Park, Sang-Hyeun and Fürnkranz, Johannes (2010):
Efficient Voting Prediction for Pairwise Multilabel Classification.
73, In: Neurocomputing, (7-9), pp. 1164 - 1176, ISSN 0925-2312, [Online-Edition: http://www.ke.tu-darmstadt.de/publications/papers/neucom10.p...],
[Article]

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.

Item Type: Article
Erschienen: 2010
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 + 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.

Journal or Publication Title: Neurocomputing
Volume: 73
Number: 7-9
Uncontrolled Keywords: efficient classification, learning by pairwise comparison, multilabel classification, voting aggregation
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Knowl­edge En­gi­neer­ing
Date Deposited: 24 Jun 2011 14:24
Official URL: http://www.ke.tu-darmstadt.de/publications/papers/neucom10.p...
Additional Information:

Advances in Computational Intelligence and Learning - 17th European Symposium on Artificial Neural Networks 2009, 17th European Symposium on Artificial Neural Networks 2009

Identification Number: doi:10.1016/j.neucom.2009.11.024
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