lvf: Las Vegas Filter
In dprep: Data Pre-Processing and Visualization Functions for Classification

Description Usage Arguments Details Value Author(s) References See Also Examples

Las Vegas Filter uses a random generation of subsets and an inconsistency measure as the evaluation function to determine the relevance of features in the dataset.

1	lvf(data, lambda, maxiter)

`data`	Name of the discretized dataset
`lambda`	Threshold for the inconsistency
`maxiter`	Maximum number of iterations

If the dataset has continuous variables, these must first be discretized. This package includes four discretization methods. A value of lambda close to the inconsistency of the whole dataset yields a large number of selected features, a large lambda yields few selected features.

bestsubset

The best subset of features

Edgar Acuna

LIU, H. and SETIONO, R. (1996). A probabilistic approach to feature selection: a filter solution. Proc. of the thirteenth International Conference of Machine Learning, 319-337.

disc.ew,inconsist,finco

#---- LVF method ----
data(iris)
iris.discew=disc.ew(iris,1:4,out="num")
inconsist(iris.discew)
lvf(iris.discew,0,100)

Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 
[1] 0.006666667
The inconsistency of the best subset is
0.006666667
The best subset of features is:
[1] 1 2 3 4