Adaptive Synthetic Sampling (ADASYN, 2008)

Generates synthetic data for minority class to balance imbalanced datasets using ADASYN.

Usage

ADASYN(x, y, k = 5, ovRate = 1)

Arguments

x: feature matrix or data.frame.
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.

Value

a list with resampled dataset.

x_new: Resampled feature matrix.
y_new: Resampled target variable.
x_syn: Generated synthetic data.
C: Number of synthetic samples for each positive class samples.

Details

Year: 2008.

Adaptive Synthetic Sampling (ADASYN) is an extension of the Synthetic Minority Over-sampling Technique (SMOTE) algorithm, which is used to generate synthetic examples for the minority class (He et al., 2008). In contrast to SMOTE, ADASYN adaptively generates synthetic examples by focusing on the minority class examples that are harder to learn, meaning those examples that are closer to the decision boundary.

Can work with classes more than 2.

References

He, H., Bai, Y., Garcia, E. A., & Li, S. (2008, June). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence) (pp. 1322-1328). IEEE.

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 <- ADASYN(x = x, y = y, k = 3)

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