%Aigaion2 BibTeX export from Knowledge Engineering Publications
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@INPROCEEDINGS{jf:PL-09-WS-Paper,
     author = {Tsoumakas, Grigorios and Loza Menc{\'{\i}}a, Eneldo and Katakis, Ioannis and Park, Sang-Hyeun and F{\"{u}}rnkranz, Johannes},
     editor = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes},
      title = {On the Combination of Two Decompositive Multi-Label Classification Methods},
  booktitle = {Proceedings of the ECML PKDD 2009 Workshop on Preference Learning (PL-09, Bled, Slovenia)},
       year = {2009},
      pages = {114--129},
        url = {/events/PL-09/09-Tsoumakas.pdf},
   abstract = {In this paper, we compare and combine two approaches for
multi-label classication that both decompose the initial problem into
sets of smaller problems. The Calibrated Label Ranking approach is
based on interpreting the multi-label problem as a preference learning
problem and decomposes it into a quadratic number of binary classiers.
The HOMER approach reduces the original problem into a hierarchy of
considerably simpler multi-label problems. Experimental results indicate
that the use of HOMER is benecial for the pairwise preference-based
approach in terms of computational cost and quality of prediction.}
}