RSLSMOTE {SMOTEWB}

Relocating safe-level SMOTE with minority outcast handling

Description

The Relocating Safe-Level SMOTE (RSLS) algorithm improves the quality of synthetic samples generated by Safe-Level SMOTE (SLS) by relocating specific synthetic data points that are too close to the majority class distribution towards the original minority class distribution in the feature space.

Usage

RSLSMOTE(x, y, k1 = 5, k2 = 5)

Arguments

x

feature matrix or data.frame.

y

a factor class variable with two classes.

k1

number of neighbors to link. Default is 5.

k2

number of neighbors to determine safe levels. Default is 5.

Details

In Safe-level SMOTE (SLS), a safe-level threshold is used to control the number of synthetic samples generated from each minority instance. This threshold is calculated based on the number of minority and majority instances in the local neighborhood of each minority instance. SLS generates synthetic samples that are located closer to the original minority class distribution in the feature space.

In Relocating safe-level SMOTE (RSLS), after generating synthetic samples using the SLS algorithm, the algorithm relocates specific synthetic data points that are deemed to be too close to the majority class distribution in the feature space. The relocation process moves these synthetic data points towards the original minority class distribution in the feature space.

This relocation process is performed by first identifying the synthetic data points that are too close to the majority class distribution. Then, for each identified synthetic data point, the algorithm calculates a relocation vector based on the distance between the synthetic data point and its k nearest minority class instances. This relocation vector is used to move the synthetic data point towards the minority class distribution in the feature space.

Note: Much faster than smotefamily::RSLS().

Value

a list with resampled dataset.

x_new

Resampled feature matrix.

y_new

Resampled target variable.

x_syn

Generated synthetic data.

C

Number of synthetic samples for each positive class samples.

Author(s)

Fatih Saglam, [e-mail]

References

Siriseriwan, W., & Sinapiromsaran, K. (2016). The effective redistribution for imbalance dataset: Relocating safe-level SMOTE with minority outcast handling. Chiang Mai J. Sci, 43(1), 234-246.

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 <- RSLSMOTE(x = x, y = y, k1 = 5, k2 = 5)

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