bnclassify: Learn discrete Bayesian network classifiers from data.

Description Details References

Description

State-of-the-art algorithms for learning discrete Bayesian network classifiers from data, with functions prediction, model evaluation and inspection.

Details

The learn more about the package, start with the vignettes: browseVignettes(package = "bnclassify"). The following is a list of available functionalities:

Structure learning algorithms:

Parameter learning methods (lp):

Model evaluating:

Predicting:

Inspecting models:

References

Bielza C and Larranaga P (2014), Discrete Bayesian network classifiers: A survey. ACM Computing Surveys, 47(1), Article 5.

Dash D and Cooper GF (2002). Exact model averaging with naive Bayesian classifiers. 19th International Conference on Machine Learning (ICML-2002), 91-98.

Friedman N, Geiger D and Goldszmidt M (1997). Bayesian network classifiers. Machine Learning, 29, pp. 131–163.

Zaidi NA, Cerquides J, Carman MJ, and Webb GI (2013) Alleviating naive Bayes attribute independence assumption by attribute weighting. Journal of Machine Learning Research, 14 pp. 1947–1988.

GI. Webb, JR Boughton, and Z Wang (2005) Not so naive bayes: Aggregating one-dependence estimators. Machine Learning, 58(1) pp. 5–24.

Hall M (2007). A decision tree-based attribute weighting filter for naive Bayes. Knowledge-Based Systems, 20(2), pp. 120-126.

Koegh E and Pazzani M (2002).Learning the structure of augmented Bayesian classifiers. In International Journal on Artificial Intelligence Tools, 11(4), pp. 587-601.

Koller D, Friedman N (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.

Pazzani M (1996). Constructive induction of Cartesian product attributes. In Proceedings of the Information, Statistics and Induction in Science Conference (ISIS-1996), pp. 66-77


bnclassify documentation built on March 26, 2020, 6:24 p.m.