Data Relocation (DatRel, 2025)

DatRel (Saglam, 2025) is a post-processing method for synthetic samples. Synthetic samples that fall outside the region covered by their class are moved back into it. The synthetic samples can come from any oversampling method; DatRel only needs the original data and the synthetic data.

Usage

DatRel(x, y, x_syn, y_syn, proportion = 1, p_of = 0)

Arguments

x

feature matrix of the original data.

y

a factor class variable of the original data. Can have more than two classes.

x_syn

feature matrix of the synthetic samples.

y_syn

factor (or vector) with the class of each synthetic sample. Every class is relocated against the union of the other classes (one-versus-rest), so any number of classes is allowed.

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 may stay slightly outside of the class region. Default is 0.

Value

a list with

x_new

Original data followed by the relocated synthetic samples.

y_new

Class variable of x_new.

x_syn

Relocated synthetic samples, in the order of the input.

y_syn

Classes of the synthetic samples.

i_dominant, x_dominant, radii_dominant, cover_proportion, n_relocated

Lists with one element per class that has synthetic samples: indices and coordinates of the dominating samples, radii of their balls, proportion of the class covered, and the number of relocated samples.

Details

Year: 2025. For every class 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; the cover algorithm is the C++ implementation of the rcccd package, which is included here (the imbalanceDatRel package is not needed any more). Each synthetic sample is assigned to the dominating ball of its class with the smallest ratio of distance to radius. If it is outside that ball, it is moved onto the border of the ball along the line to the dominating sample.

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(400, 3, 1), ncol = 2), matrix(rnorm(40, 5, 1), ncol = 2))
y <- factor(rep(c("negative", "positive"), c(200, 20)))

m0 <- SMOTE(x, y)                       # any method, or your own samples
m <- DatRel(x, y, m0$x_syn, m0$y_syn)
m$n_relocated
#> $positive
#> [1] 27
#>