%Aigaion2 BibTeX export from Knowledge Engineering Publications
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@ARTICLE{jf:Neurocomputing,
    author = {Loza Menc{\'{\i}}a, Eneldo and Park, Sang-Hyeun and F{\"{u}}rnkranz, Johannes},
  keywords = {efficient classification, learning by pairwise comparison, multilabel classification, voting aggregation},
     month = mar,
     title = {Efficient Voting Prediction for Pairwise Multilabel Classification},
   journal = {Neurocomputing},
    volume = {73},
    number = {7-9},
      year = {2010},
     pages = {1164 - 1176},
      issn = {0925-2312},
       url = {http://www.ke.tu-darmstadt.de/publications/papers/neucom10.pdf},
       doi = {10.1016/j.neucom.2009.11.024},
  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 + n d 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.},
note2={Volume: Advances in Computational Intelligence and Learning - 17th European Symposium on Artificial Neural Networks 2009, 17th European Symposium on Artificial Neural Networks 2009}
}