ubBalance: Balance wrapper

In unbalanced: Racing for Unbalanced Methods Selection

Description

The function implements several techniques to re-balance or remove noisy instances in unbalanced datasets.

Usage

ubBalance(X, Y, type="ubSMOTE", positive=1, percOver=200, percUnder=200, 
          k=5, perc=50, method="percPos", w=NULL, verbose=FALSE)

Arguments

X	the input variables of the unbalanced dataset.
Y	the response variable of the unbalanced dataset.
type	the balancing technique to use (ubOver, ubUnder, ubSMOTE, ubOSS, ubCNN, ubENN, ubNCL, ubTomek).
positive	the majority class of the response variable.
percOver	parameter used in ubSMOTE
percUnder	parameter used in ubSMOTE
k	parameter used in ubOver, ubSMOTE, ubCNN, ubENN, ubNCL
perc	parameter used in ubUnder
method	parameter used in ubUnder
w	parameter used in ubUnder
verbose	print extra information (TRUE/FALSE)

Details

The argument type can take the following values: "ubOver" (over-sampling), "ubUnder" (under-sampling), "ubSMOTE" (SMOTE), "ubOSS" (One Side Selection), "ubCNN" (Condensed Nearest Neighbor), "ubENN" (Edited Nearest Neighbor), "ubNCL" (Neighborhood Cleaning Rule), "ubTomek" (Tomek Link).

Value

The function returns a list:

X	input variables
Y	response variable
id.rm	index of instances removed if availble in the technique selected

References

Dal Pozzolo, Andrea, et al. "Racing for unbalanced methods selection." Intelligent Data Engineering and Automated Learning - IDEAL 2013. Springer Berlin Heidelberg, 2013. 24-31.

See Also

ubRacing, ubOver, ubUnder, ubSMOTE, ubOSS, ubCNN, ubENN, ubNCL, ubTomek

Examples

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

#balance the dataset
data<-ubBalance(X= input, Y=output, type="ubSMOTE", percOver=300, percUnder=150, verbose=TRUE)
balancedData<-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
Proportion of positives after ubSMOTE : 47.06 % of 1071 observations 

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