Bayesian network classifiers in Weka
Bouckaert, R. R., (2004). Bayesian network classifiers in Weka. (Working paper series. University of Waikato, Department of Computer Science. No. 14/2004). Hamilton, New Zealand: University of Waikato.
Permanent Research Commons link: http://hdl.handle.net/10289/85
Various Bayesian network classifier learning algorithms are implemented in Weka .This note provides some user documentation and implementation details. Summary of 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 classi_ers 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.
Department of Computer Science