BuildBayesNet {rWMBAT}R Documentation

Selects Features And Metafeatures Based On Mutual Info

Description

BuildBayesNet selects features and metafeatures based on mutual info

Usage

BuildBayesNet(data, class, ffactor, drop)

Arguments

data integer array containing the data used to build the Bayes net,cases in rows, variables in cols
class double column vector, the known class variable for each case
ffactor multiple of auto MI to use to threshold C->V connections
drop double, MI loss percentage threshold for testing independence. Set to.75 and adjust to filter too few/too many variable-to-variable connections

Details

This function takes a set of training data and an additional variable called "class" and tries to learn a Bayesian Network Structure by examining Mutual Information.

Value

a matrix of zeros and ones, where one in row i, column j denotes a directed link in a Bayesian network between variable i and variable j. The class variable is the last row/column.

Note

CALLED FUNCTIONS: automi(finds an MI threshold based on data) findmutualinfos(finds all values MI(VC), MI(VV) and MI(VC|V))

Author(s)

Karl Kuschner, Qian Si and William Cooke , College of William and Mary, Dept. of Physics, 2009.

References

http://kwkusc.people.wm.edu/dissertation/dissertation.htm

Examples

data(traingrpbin, traingrpclass, ff, drop) #load example data in package 
adjmat <- BuildBayesNet( traingrpbin, traingrpclass, ff, drop )

[Package rWMBAT version 2.0 Index]