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@INPROCEEDINGS{jf:PL-08-WS-Park,
     author = {Park, Sang-Hyeun and F{\"{u}}rnkranz, Johannes},
     editor = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes},
      title = {Multi-Label Classification with Label Constraints},
  booktitle = {Proceedings of the ECML PKDD 2008 Workshop on Preference Learning (PL-08, Antwerp, Belgium)},
       year = {2008},
      pages = {157--171},
        url = {http://www.mathematik.uni-marburg.de/~kebi/ws-ecml-08/12.pdf},
   abstract = {We extend the multi-label classification setting with constraints
on labels. This leads to two new machine learning tasks: First,
the label constraints must be properly integrated into the classification
process to improve its performance and second, we can try to automatically
derive useful constraints from data. In this paper, we experiment
with two constraint-based correction approaches as post-processing step
within the ranking by pairwise comparison (RPC)-framework. In addition,
association rule learning is considered for the task of label constraints
learning. We report on the current status of our work, together
with evaluations on synthetic datasets and two real-world datasets.}
}