AdaBoost-I: AdaBoost_I KEEL Imbalanced Classification Algorithm

In RKEEL: Using 'KEEL' in R Code

AdaBoost_I KEEL Imbalanced Classification Algorithm

Description

AdaBoost_I Imbalanced Classification Algorithm from KEEL.

Usage

AdaBoost_I(train, test, pruned, confidence, instancesPerLeaf,
   numClassifiers, algorithm, trainMethod, seed)

Arguments

train	Train dataset as a data.frame object
test	Test dataset as a data.frame object
pruned	pruned. Default value = TRUE
confidence	confidence. Default value = 0.25
instancesPerLeaf	instancesPerLeaf. Default value = 2
numClassifiers	numClassifiers. Default value = 10
algorithm	algorithm. Default value = "ADABOOST"
trainMethod	trainMethod. Default value = "NORESAMPLING"
seed	Seed for random numbers. If it is not assigned a value, the seed will be a random number

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::AdaBoost_I(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

RKEEL documentation built on Sept. 15, 2023, 1:08 a.m.