Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Learning Bayesian networks from incomplete data with stochastic search algorithms
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning Bayesian Networks Based on a Mutual Information Scoring Function and EMI Method
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Learning Bayesian networks with combination of MRMR criterion and EMI method
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Learning Bayesian networks using evolutionary algorithm and a variant of MDL score
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
An improved bayesian network learning algorithm based on dependency analysis
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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Currently, there are few efficient methods in practice forlearning Bayesian networks from incomplete data, whichaffects their use in real world data mining applications.This paper presents a general-duty method that estimatesthe (Conditional) Mutual Information directly from incompletedatasets, EMI. EMI starts by computing the intervalestimates of a joint probability of a variable set, which areobtained from the possible completions of the incompletedataset. And then computes a point estimate via a convexcombination of the extreme points, with weights dependingon the assumed pattern of missing data. Finally, based onthese point estimates, EMI gets the estimated (conditional)Mutual Information. This paper also applies EMI to the dependencyanalysis based learning algorithm by J. Cheng soas to efficiently learn BNs with incomplete data. The experimentalresults on Asia and Alarm networks show that EMIbased algorithm is much more efficient than two search&scoring based algorithms, SEM and EM-EA algorithms. Interms of accuracy, EMI based algorithm is more accuratethan SEM algorithm, and comparable with EM-EA algorithm.