lvf {dprep} | R Documentation

## Las Vegas Filter

### Description

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

### Usage

    lvf(data, lambda, maxiter)

### Arguments

`data` | name of the discretized dataset
`lambda` | threshold for the inconsistency
`maxiter` | maximum number of iterations

### Details

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.

### Value

`bestsubset` | the best subset of features

### Author(s)

Edgar Acuna

### References

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

### See Also

`disc.ew`,`inconsist`,`finco`

### Examples

    #---- LVF method ----
    data(my.iris)
    disciris=disc.ew(my.iris,1:6)
    inconsist(disciris)
    lvf(disciris,0,500)

[Package dprep version 1.0 Index]