Selecting weighting factors in logarithmic opinion pools
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
Complexity results and approximation strategies for MAP explanations
Journal of Artificial Intelligence Research
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
Efficient Structure Learning of Bayesian Networks using Constraints
The Journal of Machine Learning Research
Classifier chains for multi-label classification
Machine Learning
Theory refinement on Bayesian networks
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
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We present a novel approach for multilabel classification based on an ensemble of Bayesian networks. The class variables are connected by a tree; each model of the ensemble uses a different class as root of the tree. We assume the features to be conditionally independent given the classes, thus generalizing the naive Bayes assumption to the multi-class case. This assumption allows us to optimally identify the correlations between classes and features; such correlations are moreover shared across all models of the ensemble. Inferences are drawn from the ensemble via logarithmic opinion pooling. To minimize Hamming loss, we compute the marginal probability of the classes by running standard inference on each Bayesian network in the ensemble, and then pooling the inferences. To instead minimize the subset 0/1 loss, we pool the joint distributions of each model and cast the problem as a MAP inference in the corresponding graphical model. Experiments show that the approach is competitive with state-of-the-art methods for multilabel classification.