SMOTE with Boosting (SMOTEWB, 2022)

Resampling with SMOTE with boosting.

Usage

SMOTEWB(
  x,
  y,
  n_weak_classifier = 100,
  class_weights = NULL,
  k_max = NULL,
  ovRate = 1,
  ...
)

Arguments

x

feature matrix.

y

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

n_weak_classifier

number of weak classifiers for boosting.

class_weights

numeric vector of length two. First number is for positive class, and second is for negative. Higher the relative weight, lesser noises for that class. By default, \(2\times n_{neg}/n\) for positive and \(2\times n_{pos}/n\) for negative class.

k_max

to increase maximum number of neighbors. Default is ceiling(n_neg/n_pos).

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 data.

y_syn

Generated synthetic data labels.

w

Boosting weights for original dataset.

k

Number of nearest neighbors for positive class samples.

C

Number of synthetic samples for each positive class samples.

fl

"good", "bad" and "lonely" sample labels

Details

Year: 2022.

SMOTEWB (Saglam & Cengiz, 2022) is a SMOTE-based oversampling method which can handle noisy data and adaptively decides the appropriate number of neighbors to link during resampling with SMOTE.

Trained model based on this method gives significantly better Matthew Correlation Coefficient scores compared to others.

Can work with classes more than 2.

References

Sağlam, F., & Cengiz, M. A. (2022). A novel SMOTE-based resampling technique trough noise detection and the boosting procedure. Expert Systems with Applications, 200, 117023.

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 <- SMOTEWB(x = x, y = y, n_weak_classifier = 150)

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