lvf {dprep} | R Documentation |
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.
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.
#---- LVF method ---- data(my.iris) iris.discew=disc.ew(my.iris,1:6) inconsist(iris.discew) lvf(iris.discew,0,300)