ubCNN: Condensed Nearest Neighbor

In unbalanced: Racing for Unbalanced Methods Selection

Description

Condensed Nearest Neighbor selects the subset of instances that are able to correctly classifing the original datasets using a one-nearest neighbor rule.

Usage

ubCNN(X, Y, k = 1, verbose = T)

Arguments

X	the input variables of the unbalanced dataset.
Y	the response variable of the unbalanced dataset. It must be a binary factor where the majority class is coded as 0 and the minority as 1.
k	the number of neighbours to use
verbose	print extra information (TRUE/FALSE)

Details

In order to compute nearest neighbors, only numeric features are allowed.

Value

The function returns a list:

X	input variables
Y	response variable

References

P. E. Hart. The condensed nearest neighbor rule. IEEE Transactions on Informa- tion Theory, 1968.

See Also

ubBalance

Examples

library(unbalanced)
data(ubIonosphere)
n<-ncol(ubIonosphere)
output<-ubIonosphere$Class
input<-ubIonosphere[ ,-n]

data<-ubCNN(X=input, Y= output)
newData<-cbind(data$X, data$Y)

unbalanced documentation built on May 2, 2019, 7:01 a.m.