Quadrilateral Element Oversampling (GQEO, 2025)

Resampling with GQEO.

Usage

GQEO(x, y, k = 5, check_planarity = TRUE, ovRate = 1, ...)

Arguments

x: feature matrix or dataframe.
y: a factor class variable. Can have more than two classes.
k: number of nearest neighbours from which the corners of the quadrilaterals are chosen (at least 3). Default is 5.
check_planarity: if TRUE, quadrilaterals whose edges violate the polygon inequality are removed. Default is TRUE.
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: 2025.

Synthetic samples are generated inside quadrilaterals made of a sample and three of its k nearest neighbours of the same class, by bilinear interpolation of the four corners. Classes that are too small to form a quadrilateral are oversampled randomly with a warning.

References

Dai, Q., Wang, L., Zhang, J., Ding, W., & Chen, L. (2025). GQEO: Nearest neighbor graph-based generalized quadrilateral element oversampling for class-imbalance problem. Neural Networks, 184, 107107.

Author

Fatih Saglam, saglamf89@gmail.com

Examples


set.seed(1)
x <- rbind(matrix(rnorm(600, 3, 1), ncol = 2),
           matrix(rnorm(60, 5, 1), ncol = 2))
y <- as.factor(c(rep("negative", 300), rep("positive", 30)))

m <- GQEO(x = x, y = y)
table(m$y_new)
#> 
#> negative positive 
#>      300      300