oversampleDatRel: Oversampling and Data Relocation for Resampled Data

In imbalanceDatRel: Relocated Data Oversampling for Imbalanced Data Classification

Oversampling and Data Relocation for Resampled Data

Description

oversampleDatRel first oversamples using selected method then relocates resampled data using Pure and Proper Class Cover Catch Digraph.

Usage

oversampleDatRel(
  x,
  y,
  method = "SMOTE",
  proportion = 1,
  p_of = 0,
  class_pos = NULL,
  ...
)

Arguments

x	feature matrix or dataframe.
y	class factor variable.
method	oversampling method. Default is "SMOTE". Available methods are: "ADASYN": Adaptive Synthetic Sampling "ROS": Random Oversampling "ROSE": Randomly Over Sampling Examples "RSLSMOTE": Relocating safe-level SMOTE with minority outcast handling "RUS": Random Undersampling "SLSSMOTE": Safe-level Synthetic Minority Oversampling Technique "SMOTE": Synthetic Minority Oversampling Technique "SMOTEWB": SMOTE with boosting
proportion	proportion of covered samples. A real number between (0,1]. 1 by default. Smaller numbers results in less dominant samples.
p_of	proportion to increase cover radius. A real number between (0,\infty). Default is 0. Higher values tolerate other classes more.
class_pos	Class name of synthetic data. Default is NULL. If NULL, positive class is minority class.
...	arguments to be used in specified method.

Details

Oversampling using DatRel. Available oversampling methods are from SMOTEWB package. "ROSE" generates samples from all classes. DatRel relocates all class samples.

Value

an list which includes:

x_new	dominant sample indexes.
y_new	dominant samples from feature matrix, x
x_syn	Radiuses of the circle for dominant samples
i_dominant	class names
x_pos_dominant	number of classes
radii_pos_dominant	proportions each class covered

Author(s)

Fatih Saglam, [e-mail]

Examples

library(SMOTEWB)
library(rcccd)

set.seed(10)
# adding data
x <- rbind(matrix(rnorm(2000, 3, 1), ncol = 2, nrow = 1000),
           matrix(rnorm(60, 6, 1), ncol = 2, nrow = 30))
y <- as.factor(c(rep("negative", 1000), rep("positive", 30)))

# adding noise
x[1001,] <- c(3,3)
x[1002,] <- c(2,2)
x[1003,] <- c(4,4)

# resampling
m_SMOTE <- SMOTE(x = x, y = y, k = 3)

# resampled data
plot(x, col = y, main = "SMOTE")
points(m_SMOTE$x_syn, col = "green")

m_DatRel <- oversampleDatRel(x = x, y = y, method = "SMOTE")

# resampled data after relocation
plot(x, col = y, main = "SMOTE + DatRel")
points(m_DatRel$x_syn, col = "green")

imbalanceDatRel documentation built on June 8, 2025, 12:47 p.m.