Bi-directional SMOTE (BiSMOTE, 2027) — BiSMOTE • imbalanceKit

Resampling with bi-directional SMOTE.

Usage

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

Arguments

x: feature matrix or dataframe.
y: a factor class variable. Can have more than two classes.
k: number of nearest neighbors within the class that define the interpolation directions. Default is 5. Reduced automatically for classes with fewer than k + 1 samples.
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: 2027.

BiSMOTE (Saglam & Yamak, 2027) is a geometrically constrained oversampling method. For each synthetic sample, a center sample and one of its k nearest neighbors from the same class are selected. Unlike SMOTE, which places the new sample between the two, BiSMOTE interpolates bi-directionally along the line through them. The interpolation coefficient is drawn uniformly so that the synthetic sample stays within the distance from the center sample to its nearest sample of another class. In this way, generation is restricted to a locally bounded region and does not cross towards other classes, which reduces class overlap and synthetic noise.

Can work with classes more than 2.

References

Saglam, F., & Yamak, U. (2027). Bi-SMOTE: bi-directional oversampling for reducing class overlap and synthetic noise. Expert Systems with Applications, 334, 134648.

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 <- BiSMOTE(x = x, y = y, k = 5)

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