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Geometric SMOTE (GSMOTE, 2019)

GSMOTE.Rd

Resampling with GSMOTE.

Usage

GSMOTE(
  x,
  y,
  k = 5,
  alpha_sel = c("combined", "minority", "majority"),
  alpha_trunc = 0.5,
  alpha_def = 0.5,
  ovRate = 1
)

Arguments

x

feature matrix.

y

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

k

number of neighbors. Default is 5.

alpha_sel

selection method. Can be "minority", "majority" or "combined". Default is "combined".

alpha_trunc

truncation factor. A numeric value in \([-1,1]\). Default is 0.5.

alpha_def

deformation factor. A numeric value in \([0,1]\). Default is 0.5

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 feature data.

y_syn

Generated synthetic label data.

C

List with one element per class: the number of synthetic samples generated from each sample of that class.

Details

Year: 2019.

GSMOTE (Douzas & Bacao, 2019) is an oversampling method which creates synthetic samples geometrically around selected minority samples. Details are in the paper (Douzas & Bacao, 2019).

Can work with classes more than 2.

References

Douzas, G., & Bacao, F. (2019). Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE. Information sciences, 501, 118-135.

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 <- GSMOTE(x = x, y = y, k = 7)

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