Bayesian network classifiers in Weka
Citation
Export citationBouckaert, 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: https://hdl.handle.net/10289/85
Abstract
Various Bayesian network classifier learning algorithms are implemented in Weka [10].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.
Date
2004-09-01Type
Publisher
Department of Computer Science
Collections
- 2004 Working Papers [14]