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