DatRel: Data Relocation for Resampled Data using Pure and Proper...

In imbalanceDatRel: Relocated Data Oversampling for Imbalanced Data Classification

Data Relocation for Resampled Data using Pure and Proper Class Cover Catch Digraph

Description

DatRel relocates resampled data using Pure and Proper Class Cover Catch Digraph

Usage

DatRel(x, y, x_syn, proportion = 1, p_of = 0, class_pos = NULL)

Arguments

x	feature matrix or dataframe.
y	class factor variable.
x_syn	synthetic data generated by an oversampling method.
proportion	proportion of covered samples. A real number between (0,1]. 1 by default. Algorithm stops when desired percent of coverage achieved in each class. 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.

Details

Calculates cover areas using pure and proper class cover catch digraphs (PCCCD) for original dataset. Any sample outside of cover area is relocated towards a specific dominant point. Determination of dominant point to move towards is based on distance based on radii of PCCCD balls. p_of is to increase obtained radii to be more tolerant to noise. prooportion argument is cover percentage for PCCCD to stop when desired percentage is covered for each class. PCCCD models are determined using rcccd package. class_pos argument is used to specify oversampled class.

Value

an list object which includes:

x_new	Oversampled and relocated feature matrix
y_new	Oversampled class variable
x_syn	Generated and relocated sample matrix
i_dominant	Indexes of dominant samples
x_pos_dominant	Dominant samples for positive class
radii_pos_dominant	Positive class cover percentage

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)

# relocation of resampled data
m_DatRel <- DatRel(x = x, y = y, x_syn = m_SMOTE$x_syn)

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

# 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.