Synthetic Minority Oversampling Technique (SMOTE, 2002)

Resampling with SMOTE.

Usage

SMOTE(x, y, k = 5, ovRate = 1, ...)

Arguments

x: feature matrix.
y: a factor class variable. Can have more than two classes.
k: number of neighbors. Default is 5.
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.
x_syn: Generated synthetic feature data.
y_syn: Generated synthetic label data.

Details

Year: 2002.

SMOTE (Chawla et al., 2002) is an oversampling method which creates links between positive samples and nearest neighbors and generates synthetic samples along that link.

It is well known that SMOTE is sensitive to noisy data. It may create more noise.

Can work with classes more than 2.

Note: Much faster than smotefamily::SMOTE().

References

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.

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 <- SMOTE(x = x, y = y, k = 7)

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