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Local Entropy-Adversarial Oversampling (LEAO, 2025)

LEAO.Rd

Resampling with LEAO.

Usage

LEAO(
  x,
  y,
  iters = 50,
  alpha = 1,
  prob_type = c("bagging", "logistic", "knn", "nb"),
  k = 3,
  stop_prob = 0.3,
  step_max = 0.1,
  step_min = NULL,
  eps = 1e-06,
  cov_ridge = 1e-08,
  ovRate = 1,
  ...
)

Arguments

x

feature matrix or dataframe.

y

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

iters

maximum number of gradient steps per synthetic sample. Default is 50.

alpha

decay of the potential. Default is 1.

prob_type

how the class probabilities, which give the entropies, are estimated: "bagging" (classification trees, default), "logistic", "knn" or "nb" (Gaussian naive Bayes).

k

number of nearest samples of the other classes (Mahalanobis distance) from which one is chosen. Default is 3.

stop_prob

probability to stop at each step. Default is 0.3.

step_max, step_min

range of the random step size. step_min is step_max / 10 if NULL.

eps

numerical constant of the entropy. Default is 1e-6.

cov_ridge

value added to the diagonal of the covariance matrix. Default is 1e-8.

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.

Seeds are drawn with probabilities proportional to the entropy of their class probability. Each synthetic sample starts at its seed and moves by gradient descent on a potential that is high near the seed and low near a nearby sample of the other classes, until it is not positive, the maximum number of steps is reached or the random stop happens.

The method is defined for two classes. With more than two classes every class is oversampled against the union of the other classes (one-versus-rest).

References

Tao, L. (2025). Local entropy-adversarial oversampling for imbalanced datasets. Neurocomputing, 131959.

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