Bayesian Network Classifiers in Weka
for Version 3-5-7
Remco R. Bouckaert
©2006-2007 University of Waikato
Various Bayesian network classifier learning algorithms are implemented in Weka [12
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 classifiers like C4.5, nearest neighbor, support vector, etc.
- Source code available under GPL1 allows for integration in other open-source systems and makes it easy to extend.