Random Over-Sampling Examples (ROSE, 2014)

Generates synthetic data for each class to balance imbalanced datasets using kernel density estimations. Can be used for multiclass datasets.

Usage

ROSE(x, y, h = 1, ovRate = 1)

Arguments

x

feature matrix or data.frame.

y

a factor class variable. Can be multiclass.

h

A numeric vector of length one or number of classes in y. If one is given, all classes will have same shrink factor. If a value is given for each classes, it will match respectively to levels(y). Default is 1.

ovRate

Oversampling rate. Class \(j\) receives round(ovRate * (n_max - n_j)) synthetic samples, where n_max is the size of the largest class (halves are rounded up). Default is 1 meaning full balance. A vector with one value per class is also accepted.

Value

a list with resampled dataset.

x_new

Resampled feature matrix.

y_new

Resampled target variable.

Details

Year: 2014.

Randomly Over Sampling Examples (ROSE) (Menardi and Torelli, 2014) is an oversampling method which uses conditional kernel densities to balance dataset. There is already an R package called `ROSE` (Lunardon et al., 2014). Difference is that this one is much faster and can be applied for more than two classes.

References

Lunardon, N., Menardi, G., and Torelli, N. (2014). ROSE: a Package for Binary Imbalanced Learning. R Jorunal, 6:82–92.

Menardi, G. and Torelli, N. (2014). Training and assessing classification rules with imbalanced data. Data Mining and Knowledge Discovery, 28:92–122.

Author

Fatih Saglam, saglamf89@gmail.com

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 <- ROSE(x = x, y = y, h = c(0.12, 1))

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