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Random Oversampling (ROS)

ROS.Rd

Resampling with ROS.

Usage

ROS(x, y, ovRate = 1, ...)

Arguments

x

feature matrix.

y

a factor class variable. Can have more than two classes.

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.

...

not used.

Value

a list with resampled dataset.

x_new

Resampled feature matrix.

y_new

Resampled target variable.

Details

Random Oversampling (ROS) is a method of copying and pasting of positive samples until balance is achieved.

Can work with classes more than 2.

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 <- ROS(x = x, y = y)

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