@inproceedings{6631,
  title = {Multi-Label Learning by Exploiting Label Dependency},
  journal = {Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010)},
  abstract = {In multi-label learning, each training example is associated
  with a set of labels and the task is to predict the proper label
  set for the unseen example. Due to the tremendous (exponential)
  number of possible label sets, the task of learning
  from multi-label examples is rather challenging. Therefore,
  the key to successful multi-label learning is how to effectively
  exploit correlations between different labels to facilitate
  the learning process. In this paper, we propose to use a
  Bayesian network structure to efficiently encode the condi-
  tional dependencies of the labels as well as the feature set,
  with the feature set as the common parent of all labels. To
  make it practical, we give an approximate yet efficient procedure
  to find such a network structure. With the help of this
  network, multi-label learning is decomposed into a series of
  single-label classification problems, where a classifier is constructed
  for each label by incorporating its parental labels
  as additional features. Label sets of unseen examples are
  predicted recursively according to the label ordering given
  by the network. Extensive experiments on a broad range of
  data sets validate the effectiveness of our approach against
  other well-established methods.},
  pages = {999-1008},
  editors = {Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jul,
  year = {2010},
  slug = {6631},
  author = {Zhang, M-L. and Zhang, K.},
  month_numeric = {7}
}