Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Elements of information theory
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Wrappers for feature subset selection
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Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Eighteenth national conference on Artificial intelligence
Natural statistical models for automatic speech recognition
Natural statistical models for automatic speech recognition
Discriminative, generative and imitative learning
Discriminative, generative and imitative learning
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Discriminative versus generative parameter and structure learning of Bayesian network classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
When discriminative learning of Bayesian network parameters is easy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Bayesian network classifiers versus selective k-NN classifier
Pattern Recognition
Hybrid parallel classifiers for semantic subspace learning
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
A fast subspace text categorization method using parallel classifiers
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Hybrid classifiers based on semantic data subspaces for two-level text categorization
International Journal of Hybrid Intelligent Systems
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Recently, methods for discriminative learning of Bayesian networks used for classification, i.e. learning the structure and/or parameters by optimizing the class conditional probability directly, have been proposed. In this paper, we use a simple order-based greedy algorithm for learning a discriminative network structure. First, we establish an ordering of the features according to the information for classification. Given this ordering, we can find the structure consistent with this ordering in polynomial time. We introduce a new information theoretic score to learn the structure of a Bayesian network from an ordering. Furthermore, we provide a heuristic method for subsequent pruning of the learned network structure. This reduces the number of parameters and the performance may even improve due to overfitting effects, especially when the sample size for learning is small. Experiments have been performed on 25 data sets from the UCI repository. The experiments suggest that the discriminative structure found by our algorithm outperforms on average other generative and discriminative structure learning approaches.