%Aigaion2 BibTeX export from Knowledge Engineering Publications
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     author = {Loza Menc{\'{\i}}a, Eneldo and Park, Sang-Hyeun and F{\"{u}}rnkranz, Johannes},
      month = apr,
      title = {Efficient Voting Prediction for Pairwise Multilabel Classification},
  booktitle = {Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN 2009, Bruges, Belgium)},
       year = {2009},
      pages = {117--122},
  publisher = {d-side publications},
       isbn = {2-930307-09-9},
        url = {http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2009-112.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 n^2 to n + dn log n, where n is the total number of labels and d is the average number of labels, which is typically quite small in real-world datasets.},