do.mifs | R Documentation |
MIFS is a supervised feature selection that iteratively increases the subset of variables by choosing maximally informative feature based on the mutual information.
do.mifs( X, label, ndim = 2, beta = 0.75, discretize = c("default", "histogram"), preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate") )
X |
an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. |
label |
a length-n vector of class labels. |
ndim |
an integer-valued target dimension. |
beta |
penalty for relative importance of mutual information between the candidate and already-chosen features in iterations. Author proposes to use a value in (0.5,1). |
discretize |
the method for each variable to be discretized. The paper proposes |
preprocess |
an additional option for preprocessing the data. Default is "null". See also |
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a length-ndim vector of indices with highest scores.
a list containing information for out-of-sample prediction.
a (p\times ndim) whose columns are basis for projection.
Kisung You
battiti_using_1994Rdimtools
## use iris data ## it is known that feature 3 and 4 are more important. data(iris) iris.dat = as.matrix(iris[,1:4]) iris.lab = as.factor(iris[,5]) ## try different beta values out1 = do.mifs(iris.dat, iris.lab, beta=0) out2 = do.mifs(iris.dat, iris.lab, beta=0.5) out3 = do.mifs(iris.dat, iris.lab, beta=1) ## visualize opar <- par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(out1$Y, pch=19, col=iris.lab, main="beta=0") plot(out2$Y, pch=19, col=iris.lab, main="beta=0.5") plot(out3$Y, pch=19, col=iris.lab, main="beta=1") par(opar)
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