Finding MAPs for belief networks is NP-hard
Artificial Intelligence
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Learning Markov networks: maximum bounded tree-width graphs
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Collective multi-label classification
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Multi-Objective Learning of Multi-Dimensional Bayesian Classifiers
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Inference and Learning in Multi-dimensional Bayesian Network Classifiers
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
Learning tree structure of label dependency for multi-label learning
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Multi-label relational neighbor classification using social context features
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Learning compact hashing codes for efficient tag completion and prediction
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ACM Transactions on the Web (TWEB)
Probabilistic multi-label classification with sparse feature learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Pattern Recognition Letters
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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.