mRMR_disc {dprep} | R Documentation |
This function selects the best predictors using the minimum redundancy and Maximum relevance method.
mRMR_disc(datos, size = 3, criterion = c("MIQ", "MID"))
datos |
The dataset |
size |
The number of featires to be selected |
criterion |
Either MIQ or MID |
This a hybrid feature selection method that chooses as the best features as the one that have high Mutual Infomation with the classes (V) and low mutual information among them (W). These two measures are combined two criteria: MIQ= that maximizes V/w and MID that maximizes V-W.
selec_list |
List of best features for prediction |
Edgar Acuna
Peng, H.C., Long, F., and Ding, C. Feature selection based on mutual information. criteria of max-dependency, max-relevance, and min-redundancy.
data(bupa) bupad=disc.ew(bupa,1:6,out="num") mRMR_disc(bupad,3,criterion="MIQ")