SMOTEWB: SMOTE with boosting (SMOTEWB)

In SMOTEWB: Imbalanced Resampling using SMOTE with Boosting (SMOTEWB)

SMOTE with boosting (SMOTEWB)

Description

Resampling with SMOTE with boosting.

Usage

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

Arguments

x	feature matrix.
y	a factor class variable with 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).
...	additional inputs for ada::ada().

Details

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.

Value

a list with resampled dataset.

x_new	Resampled feature matrix.
y_new	Resampled target variable.
x_syn	Generated synthetic data.
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.

Author(s)

Fatih Saglam, [e-mail]

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.

Can work with 2 classes only yet.

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)

SMOTEWB documentation built on May 29, 2024, 11:15 a.m.