Efficient voting prediction for pairwise multilabel classification

  title={Efficient voting prediction for pairwise multilabel classification},
  author={Eneldo Loza Menc{\'i}a and Sang-Hyeun Park and Johannes F{\"u}rnkranz},
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… CONTINUE READING

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