Over several years, I have developed open-source machine learning software for inductively learning various forms of Bayesian networks from data, and for classification using BNs.
- Learn Naive Bayes, Tree-Augmented Naive Bayes (TAN), General Bayesian Networks, and BN-Augmented Naive Bayes structures
- Multiple scoring metrics, including the K2 metric, BDeu and CMI
- Visualisation of structures using GraphViz (DOT and DOTTY)
- Supplementary code to work with data from other formats, to perform cross-validation, to construct learning curves, and to construct ROC curves
For a description of the classifiers, and a discussion of their relative performance, see: “On the Classification Performance of TAN and General Bayesian Networks” , Michael G. Madden. Knowledge Based Systems, 2009.
Please cite that paper if you use this software in your work.