Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning - Special issue on learning with probabilistic representations
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Bayesian network is a powerful tool of feature subset selection. Feature subset selection based on Bayesian network is to build the Markov blanket of class variable. In this paper, feature subset selection is done based on local dependency analysis method. First, basic dependency relationships between variables, basic structures between nodes, dependency separation criterion and the Markov blanket are analyzed. Then the Markov blanket of class variables is learned by dependency analysis. Finally, it is proved that learned feature subset is the Markov blanket of class variables under some assumptions. Experiments show that the method is more flexible, efficient and reliable than existing feature subset selection based on Bayesian network.