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
Machine Learning - Special issue on learning with probabilistic representations
Applying general Bayesian techniques to improve TAN induction
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A new Bayesian tree learning method with reduced time and space complexity
Fundamenta Informaticae
Bayesian Network Mining System
Proceedings of the International Symposium on "Intelligent Information Systems X"
Learning with mixtures of trees
The Journal of Machine Learning Research
Feature subset selection by genetic algorithms and estimation of distribution algorithms
Artificial Intelligence in Medicine
Very large Bayesian multinets for text classification
Future Generation Computer Systems
Very large Bayesian multinets for text classification
Future Generation Computer Systems
Very large Bayesian networks in text classification
ICCS'03 Proceedings of the 1st international conference on Computational science: PartI
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A well-known problem with Bayesian networks (BN) is the practical limitation for the number of variables for which a Bayesian network can be learned in reasonable time. Even the complexity of simplest tree-like BN learning algorithms is prohibitive for large sets of variables. The paper presents a novel algorithm overcoming this limitation for the tree-like class of Bayesian networks. The new algorithm space consumption grows linearly with the number of variables n while the execution time is proportional to n ln(n), out performing any known algorithm. This opens new perspectives in construction of Bayesian networks from data containing tens of thousands and more variables, e.g. in automatic text categorization.