RWO: Random Walk Oversampling (SMOTE)

In SMOTEWB: Imbalanced Resampling using SMOTE with Boosting (SMOTEWB)

Random Walk Oversampling (SMOTE)

Description

Resampling with RWO

Usage

RWO(x, y)

Arguments

x	feature matrix.
y	a factor class variable with two classes.

Details

RWO (Zhang and Li, 2014) is an oversampling method which generates data using variable standard error in a way that it preserves the variances of all variables.

Can work with classes more than 2.

Value

a list with resampled dataset.

x_new	Resampled feature matrix.
y_new	Resampled target variable.
x_syn	Generated synthetic feature data.
y_syn	Generated synthetic label data.

Author(s)

Fatih Saglam, [e-mail]

References

Zhang, H., & Li, M. (2014). RWO-Sampling: A random walk over-sampling approach to imbalanced data classification. Information Fusion, 20, 99-116.

Examples

set.seed(1)
x <- rbind(matrix(rnorm(2000, 3, 1), ncol = 2, nrow = 1000),
           matrix(rnorm(100, 5, 1), ncol = 2, nrow = 50))
y <- as.factor(c(rep("negative", 1000), rep("positive", 50)))

plot(x, col = y)

# resampling
m <- RWO(x = x, y = y)

plot(m$x_new, col = m$y_new)

SMOTEWB documentation built on May 29, 2024, 11:15 a.m.