State-of-the-art algorithms for learning discrete Bayesian network classifiers from data, with functions prediction, model evaluation and inspection.
Maintainer: Mihaljevic Bojan boki.mihaljevic@gmail.com [copyright holder]
Authors:
Bielza Concha mcbielza@fi.upm.es
Larranaga Pedro pedro.larranaga@fi.upm.es
Other contributors:
Wickham Hadley (some code extracted from memoise package) [contributor]
The learn more about the package, start with the vignettes:
browseVignettes(package = "bnclassify"). The following is a list of available
functionalities:
Structure learning algorithms:
nb: Naive Bayes (Minsky, 1961)
tan_cl: Chow-Liu's algorithm for one-dependence estimators (CL-ODE) (Friedman et al., 1997)
fssj: Forward sequential selection and joining (FSSJ) (Pazzani, 1996)
bsej: Backward sequential elimination and joining (BSEJ) (Pazzani, 1996)
tan_hc: Hill-climbing tree augmented naive Bayes (TAN-HC) (Keogh and Pazzani, 2002)
tan_hcsp: Hill-climbing super-parent tree augmented naive Bayes (TAN-HCSP) (Keogh and Pazzani, 2002)
aode: Averaged one-dependence estimators (AODE) (Webb et al., 2005)
Parameter learning methods (lp):
Bayesian and maximum likelihood estimation
Weighting attributes to alleviate naive bayes' independence assumption (WANBIA) (Zaidi et al., 2013)
Attribute-weighted naive Bayes (AWNB) (Hall, 2007)
Model averaged naive Bayes (MANB) (Dash and Cooper, 2002)
Model evaluating:
cv: Cross-validated estimate of accuracy
logLik: Log-likelihood
AIC: Akaike's information criterion (AIC)
BIC: Bayesian information criterion (BIC)
Predicting:
predict: Inference for complete and/or incomplete data (the latter through gRain)
Inspecting models:
plot: Structure plotting (through igraph)
print: Summary
params: Access conditional probability tables
nparams: Number of free parameters
and more. See inspect_bnc_dag and inspect_bnc_bn.
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
Useful links: