Very large Bayesian multinets for text classification
This paper presents a newly developed algorithm learning very large tree-like Bayesian networks from data and exploits it to create a Bayesian multinet (BMN) classifier for natural language text documents. Results of empirical evaluation of this BMN classifier are presented. The study suggests that tree-like Bayesian networks are able to handle a classification task in 100000 variables with sufficient speed and accuracy.
bayesian multinets classification