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Multi-Label Learning by Exploiting Label Dependency


Conference Paper


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.

Author(s): Zhang, M-L. and Zhang, K.
Journal: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010)
Pages: 999-1008
Year: 2010
Month: July
Day: 0
Editors: Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1835804.1835930
Event Name: 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010)
Event Place: Washington, DC, USA

Address: New York, NY, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Multi-Label Learning by Exploiting Label Dependency},
  author = {Zhang, M-L. and Zhang, K.},
  journal = {Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010)},
  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},
  month_numeric = {7}