Bayesian Network Classifiers in Weka
for Version 3-5-7

Remco R. Bouckaert

remco@cs.waikato.ac.nz

©2006-2007 University of Waikato

Abstract:

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.