Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Multi-Label Classification Using Conditional Dependency Networks

In this paper, we tackle the challenges of multilabel classification by developing a general conditional dependency network model. The proposed model is a cyclic directed graphical model, which provides an intuitive representation for the dependencies among multiple label variables, and a well integrated framework for efficient model training using binary classifiers and label predictions using Gibbs sampling inference. Our experiments show the proposed conditional model can effectively exploit the label dependency to improve multilabel classification performance.