bal_oversampling: Random or SMOTE-based class oversampling

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Random or SMOTE-based class oversampling

Description

Balance class distributions by randomly replicating minority examples or by generating synthetic samples with a local SMOTE implementation.

Usage

bal_oversampling(attribute, method = c("smote", "random"), k = 5)

Arguments

attribute	target class attribute name
method	oversampling strategy: "smote" or "random"
k	number of nearest neighbors used by the SMOTE strategy

Value

returns an object of class bal_oversampling

References

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique.

Examples

data(iris)
iris_imb <- iris[c(1:50, 51:71, 101:111), ]
bal <- bal_oversampling("Species", method = "smote")
iris_bal <- transform(bal, iris_imb)
table(iris_bal$Species)

daltoolbox documentation built on May 14, 2026, 9:06 a.m.