IRIC： Integrated R Library for Imbalanced Classification

IRIC is an R library for imbalanced classification, which will bring convenience to users by integrating a wide set of solutions into one library.

• Introduction
• Installation
• Examples

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Introduction

The current version of IRIC (v1.2) provides a set of 19 approaches for imbalanced classification, in which 8 approaches are new implementations in R. All these approaches can be classified into 3 strategies: data level, algorithm level and ensemble-based strategy. In addition, we provide parallel implementations of Bagging-based solution to improve the efficiency of model building. All approaches in IRIC are presented in the table below.

Strategy	Submodule	Method
Algorithm level	Cost-sensitive learning	CSC4.5
Data level	Oversampling	ADASYN, MWMOTE, Random Oversampling, SMOTE
	Undersampling	CLUS，Random Undersampling
	Hybrid Sampling	SmoteENN, SmoteTL, SPIDER
Ensemble-based learning	BalanceBagging	RBBagging, ROSBagging,RUSBagging,SMOTEBagging
	BalanceBoost	AdaC2, RUSBoost, SMOTEBoost
	Hybrid Ensemble	BalanceCascade, EasyEnsemble

Installation

R version >= 3.5

library(devtools)
devtools::install_github('reneroliveira/IRIC')

Examples

SMOTE(Data level), CSC4.5 (Algorithm level) and RBBagging (Ensemble-based level) are presented as examples of IRIC's usage.

• SMOTE
• CSC4.5
• RBBagging

SMOTE

#Example of SMOTE
#Load the package caret for data partitioning
library(caret)
#Load data set
data(Korean)
#data split
sub <- createDataPartition(Korean$Churn,p=0.75,list=FALSE)
trainset <- Korean[sub,]
testset <- Korean[-sub,]
#call the SMOTE
newData<- SMOTE(Churn~., trainset)

CSC4.5

#Example of CSC4.5 
library(caret)
#Load data set
data(Korean)
#Data split
sub <- createDataPartition(Korean$Churn,p=0.75,list=FALSE)
trainset <- Korean[sub,]
testset <- Korean[-sub,]
x <- trainset[, -11]
y <- trainset[, 11]
#training model
model <- CSC45(x, y, pruning = TRUE)
#Prediction
output <- predict (model, x) 

RBBagging

#Example of RBBagging 
#Load the package caret for data partitioning
library (caret) 
#Load data set 
data(Korean)
#Data split
sub <- createDataPartition(Korean$Churn, p=0.75,list=FALSE)
trainset <- Korean[sub,]
testset <- Korean[-sub,]
x <- trainset[, -11]
y <- trainset[, 11]
#call the RBBaging for model training train 
model <- bbagging(x, y, type="RBBagging", allowParallel=TRUE)
#prediction
output <- predict(model, x)