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Evaluation-Based SMOTE (EBSMOTE, 2019)

EBSMOTE.Rd

Resampling with EBSMOTE.

Usage

EBSMOTE(
  x,
  y,
  m_alpha = 5,
  m_beta = 5,
  lambda = 3,
  v_alpha = 5,
  w_alpha = 5,
  expand_alpha = 0.3,
  ovRate = 1,
  ...
)

Arguments

x

feature matrix or dataframe.

y

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

m_alpha, m_beta

number of nearest neighbours that decide whether a sample of the class (m_alpha) or of the other classes (m_beta) is on the border. Default is 5.

lambda

a sample is on the border if at least m / lambda but not all of its m nearest neighbours are from the other side. Default is 3.

v_alpha

number of the nearest borderline samples of the other side from which one is selected. Default is 5.

w_alpha

number of the nearest borderline samples of the class, around the selected other side sample, from which one is selected. Default is 5.

expand_alpha

weight of the other side sample in the generated sample, in \([0, 1)\). Default is 0.3.

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: 2019.

A borderline sample of the class, a nearby borderline sample of the other classes and a borderline sample of the class near that one are combined: the generated sample is \((1 - \alpha)(\phi p_i + (1 - \phi) p_k) + \alpha t_j\) with a uniform \(\phi\). If no borderline samples exist, random oversampling is used with a warning.

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

Hussein, A. S., Diallo, B., & Liu, J. (2019). EBSMOTE: Evaluation-based synthetic minority oversampling technique for imbalanced dataset learning. In 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) (pp. 1226-1233). IEEE.

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