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 distribution P ( 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.