ubOSS: One Side Selection

In unbalanced: Racing for Unbalanced Methods Selection

Description

One Side Selection is an undersampling method resulting from the application of Tomek links followed by the application of Condensed Nearest Neighbor.

Usage

ubOSS(X, Y, verbose = TRUE)

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.
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

M. Kubat, S. Matwin, et al. Addressing the curse of imbalanced training sets: one-sided selection. In MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, pages 179-186. MORGAN KAUFMANN PUBLISHERS, INC., 1997.

See Also

ubBalance

Examples

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

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

Example output

Loading required package: mlr
Loading required package: ParamHelpers
Loading required package: foreach
Loading required package: doParallel
Loading required package: iterators
Loading required package: parallel
Instances removed 31 : 14.62 % of 0 class ; 9.17 % of training ; Time needed 0 

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