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Efficient voting prediction for pairwise multilabel classification
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DOI：10.1016/j.neucom.2009.11.024.png)
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Abstract
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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 possible labels and d is the average number of labels per instance, which is typically quite small in real-world datasets. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Multilabel classification
Voting aggregation
Learning by pairwise comparison
Efficient classification
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