2011-12-01
Multi-label classification using conditional dependency networks
Publication
Publication
Presented at the
22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 (July 2011), Barcelona, Catalonia
In this paper, we tackle the challenges of multi-label 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 multi-label classification performance.
Additional Metadata | |
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doi.org/10.5591/978-1-57735-516-8/IJCAI11-220 | |
22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 | |
Organisation | School of Computer Science |
Guo, Y, & Gu, S. (Suicheng). (2011). Multi-label classification using conditional dependency networks. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1300–1305). doi:10.5591/978-1-57735-516-8/IJCAI11-220
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