greedy_wrapper: Learn Bayesian network classifiers in a a greedy wrapper...

In bnclassify: Learning Discrete Bayesian Network Classifiers from Data

Learn Bayesian network classifiers in a a greedy wrapper fashion.

Description

Greedy wrapper algorithms for learning Bayesian network classifiers. All algorithms use cross-validated estimate of predictive accuracy to evaluate candidate structures.

Usage

fssj(class, dataset, k, epsilon = 0.01, smooth = 0, cache_reset = NULL)

bsej(class, dataset, k, epsilon = 0.01, smooth = 0, cache_reset = NULL)

tan_hc(class, dataset, k, epsilon = 0.01, smooth = 0, cache_reset = NULL)

kdb(
  class,
  dataset,
  k,
  kdbk = 2,
  epsilon = 0.01,
  smooth = 0,
  cache_reset = NULL
)

tan_hcsp(class, dataset, k, epsilon = 0.01, smooth = 0, cache_reset = NULL)

Arguments

class	A character. Name of the class variable.
dataset	The data frame from which to learn the classifier.
k	An integer. The number of folds.
epsilon	A numeric. Minimum absolute improvement in accuracy required to keep searching.
smooth	A numeric. The smoothing value (\alpha) for Bayesian parameter estimation. Nonnegative.
cache_reset	A numeric. Number of iterations after which to reset the cache of conditional probability tables. A small number reduces the amount of memory used. NULL means the cache is never reset (the default).
kdbk	An integer. The maximum number of feature parents per feature.

Value

A bnc_dag object.

References

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

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.

Examples

data(car)
tanhc <- tan_hc('class', car, k = 5, epsilon = 0)  
## Not run: plot(tanhc)
  

bnclassify documentation built on June 22, 2024, 11:08 a.m.