finco: FINCO Feature Selection Algorithm
In dprep: Data Pre-Processing and Visualization Functions for Classification

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

This function selects features using the FINCO algorithm. The dataset must contain only discretized values.

1	finco(data,level)

`data`	Name of the dataset containing the discretized values
`level`	Minimum inconsistency level

The level value must be greater than the inconsistency of the whole dataset, which first must be discretized. The function inconsist included in this library computes inconsistencies. A small value for level yields a greater number of selected features.

`varselec`	Index of selected features
`inconsis`	Inconsistency rates of the selected features

Edgar Acuna

Acuna, E , (2003) A comparison of filters and wrappers for feature selection in supervised classification. Proceedings of the Interface 2003 Computing Science and Statistics. Vol 34.

Acuna, E., Coaquira, F. and Gonzalez, M. (2003). A comparison of feature selection procedures for classifiers based on kernel density estimation. Proc. of the Int. Conf. on Computer, Communication and Control technologies, CCCT03. VolI. p. 468-472. Orlando, Florida.

inconsist,lvf

1 2 3 4 5	#---- Feature Selection with FINCO data(iris) iris.discew=disc.ew(iris,1:6,out="num") inconsist(iris.discew) finco(iris.discew,0.05)

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
features selected and their inconsistency  rates
$varselec
[1] 3

$inconsis
[1] 0.05333333