Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
412890 | Neurocomputing | 2010 | 13 Pages |
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 n2 to n+dnlogn, 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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Eneldo Loza Mencía, Sang-Hyeun Park, Johannes Fürnkranz,