Article ID Journal Published Year Pages File Type
412890 Neurocomputing 2010 13 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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