Oversampling followed by DatRel (2025)

Generates synthetic samples with a base method and post-processes them with DatRel. DatRel (Saglam, 2025) makes the synthetic samples of any oversampling method safer: synthetic samples that fall outside the regions that the class covers are moved back into them. DatRel is not an oversampling method itself; it relocates the samples generated by a base method.

Usage

oversampleDatRel(
  x,
  y,
  method = "SMOTE",
  ovRate = 1,
  proportion = 1,
  p_of = 0,
  ...
)

Arguments

x

feature matrix.

y

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

method

base oversampling method that generates the synthetic samples. One of "ROS", "RWO", "ROSE", "SMOTE", "ADASYN", "BLSMOTE", "SLSMOTE", "RSLSMOTE", "GSMOTE", "SMOTEWB", "BiSMOTE", "MCSMOTE", "EBSMOTE", "GQEO", "LEAO", "SyMProD", "RadiusSMOTE" or "VSSMOTE". Default is "SMOTE".

ovRate

Oversampling rate, as in the base method: class \(j\) receives round(ovRate * (n_max - n_j)) synthetic samples. Default is 1.

proportion

proportion of the samples of a class that has to be covered by the dominating balls. Default is 1 (all samples).

p_of

overflow proportion: the radii of the balls are multiplied by 1 + p_of, so the synthetic samples are allowed to stay slightly outside of the class region. Default is 0.

...

arguments of the base method, for example k = 7 for method = "SMOTE".

Value

a list with resampled dataset.

x_new

Resampled feature matrix.

y_new

Resampled target variable.

x_syn

Generated (relocated) synthetic feature data.

y_syn

Generated synthetic label data.

i_dominant

List (one element per class) of the indices of the dominating samples within the class.

x_dominant

List of the dominating samples.

radii_dominant

List of the radii of the dominating balls.

cover_proportion

List of the proportions of the class covered.

n_relocated

List of the numbers of relocated synthetic samples.

Details

Year: 2025. For every class (one-vs-rest for more than two classes) a ball is placed around each sample whose radius is the distance to the nearest sample of the other classes (times 1 + p_of), so that the ball contains no sample of another class. A small set of dominating samples whose balls cover the class (pure and proper class cover catch digraph, PCCCD) is selected greedily. Each synthetic sample is assigned to the dominating ball with the smallest ratio of distance to radius. If the sample is outside of that ball, it is moved onto the border of the ball along the line to the dominating sample. The cover algorithm is the C++ implementation of the rcccd package, which is included here.

References

Saglam, F. (2025). DatRel: a noise-tolerant data relocation approach for effective synthetic data generation in imbalanced classifiers. Machine Learning. doi:10.1007/s10994-025-06755-8

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)))

m <- oversampleDatRel(x = x, y = y, method = "SMOTE", k = 7)
m$n_relocated
#> $negative
#> NULL
#> 
#> $positive
#> [1] 232
#> 
plot(m$x_new, col = m$y_new)