Bayesian network classifiers in Weka
Working Paper No. 14/2004
Various Bayesian network classifier learning algorithms are implemented in Weka. This note provides some user documentation and implementation details. Summary of the main capabilities: * Structure learning of Bayesian networks using various hill climbing (K2, B, etc) and general purpose (simulated annealing, tabu search) algorithms. * Local score metrics implemented; Bayes, BDe, MDL, entropy, AIC. * Global score metrics implemented; leave one out cv, k-fold cv and cumulative cv. * Conditional independence based causal recovery algorithm available. * Parameter estimation using direct estimates and Bayesian model averaging. * GUI for easy inspection of Bayesian networks. * Part of Weka allowing systematic experiments to compare Bayes net performance with general purpose classifiers like C4.5, nearest neighbor, support vector, etc. * Source code available under GPL allows for integration in other systems and makes it easy to extend.