A Mixtures-of-Trees Framework for Multi-Label Classification.
We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distributionP(Y|X). Our approach uses a mixture of tree-structured Bayesian networks, which can leverage the computational advantages of conditional tree-structured models and the abilities of mixtures to compensate for tree-structured restrictions. We develop algorithms for learning the model from data and for performing multi-label predictions using the learned model. Experiments on multiple datasets demonstrate that our approach outperforms several state-of-the-art multi-label classification methods.
Computer Science Dept., University of Pittsburgh, Pittsburgh, PA, USA.
Charmgil
C
Hong
GE Global Research, San Ramon, CA, USA.
Iyad
I
Batal
Computer Science Dept., University of Pittsburgh, Pittsburgh, PA, USA.
Milos
M
Hauskrecht
Bayesian network; Mixture of trees; Multi-label classification