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.
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 selectiona filter solution. Proc. of the thirteenth International Conference of Machine Learning, 319-337.
#---- LVF method ---- data(my.iris) disciris=disc.ew(my.iris,1:6) inconsist(disciris) lvf(disciris,0,500)