rrfc4: Roughenen Random Forests - C4 (RRFC4)

Description

RRFC4 algorithm

1.Impose missing values under the mechanism of missing completely at random on all covariates of the training dataset.

2.Hot-deck imputation for all variables. For each variable, observed values are randomly selected to impute missing values.

3.Build one tree in random forests using the above imputed training dataset, and then use it to predict the binary outcomes in the original testing dataset.

4.Repeat 1 to 3 for number.trees times.

Usage

rrfc4(dat, yvar = ncol(dat), tr, te, mispct, number.trees)

Arguments

dat

A data frame containing both training and testing datasets

yvar

The column number of the binary outcome variable, a factor variable. The default value is set as ncol(dat)

Row numbers of all training data

Row numbers of all testing data

mispct

Rate of missing data, ranging from 0 to 1

number.trees

Number of trees used in roughened random forests

Value

A prediction matrix. Each column shows the predicted values by a single tree. Each row is sequentially associated with the observations in the testing dataset. Each cell value is either 0 or 1.

References

Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

Liaw, A. & Wiener, M., 2002. Classification and regression by randomForest. R News, 2(3), pp. 18-22.

Xiong, Kuangnan. "Roughened Random Forests for Binary Classification." PhD diss., State University of New York at Albany, 2014.

Examples

Run this code

if(require(MASS)){
if(require(caTools)){

dat=rbind(Pima.tr,Pima.te)
number.trees=50
#number.trees=500
tr=1:200
te=201:532
mispct=0.4
yvar=ncol(dat)
  
#AUC value for the testing dataset based on the original random forests
rf=randomForest(dat[tr,-yvar],dat[tr,yvar],dat[te,-yvar],ntree=number.trees)
print(colAUC(rf$test$votes[,2],dat[te,yvar]))

#AUC value for the testing dataset based on RRFC4
pred.rrfc4=rrfc4(dat,yvar,tr,te,mispct,number.trees)
print(colAUC(apply(pred.rrfc4$pred,1,mean),dat[te,yvar]))
}}
#

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