| Type: | Package |
| Title: | Relocated Data Oversampling for Imbalanced Data Classification |
| Version: | 0.1.6 |
| Description: | Relocates oversampled data from a specific oversampling method to cover area determined by pure and proper class cover catch digraphs (PCCCD). It prevents any data to be generated in class overlapping area. For more details, see the corresponding publication: F. Sağlam (2025) <doi:10.1007/s10994-025-06755-8>. |
| Depends: | R (≥ 4.2), rcccd |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | RANN, Rfast, SMOTEWB |
| URL: | https://doi.org/10.1007/s10994-025-06755-8 |
| NeedsCompilation: | no |
| Packaged: | 2025-05-08 10:16:27 UTC; sagla |
| Author: | Fatih Saglam 
[aut, cre] |
| Maintainer: | Fatih Saglam <saglamf89@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-05-08 11:20:01 UTC |
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 |
p_of |
proportion to increase cover radius. A real number between
|
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, saglamf89@gmail.com
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")
Determining cover balls
Description
Determining cover balls
Usage
f_dominate(x_main, x_other, proportion = 1, p_of = 0)
Arguments
x_main |
Target class samples. |
x_other |
Non-target class samples. |
proportion |
proportion of covered samples. A real number between |
p_of |
roportion to increase cover radius. A real number between
|
Details
To be used in DatRel.
Value
a list object with following:
i_dominant |
dominant sample indexes |
dist_main2other |
distance matrix of target class samples to non-target class samples |
dist_main2main |
distance matrix of target class samples to target class samples |
Author(s)
Fatih Saglam, saglamf89@gmail.com
Relocation function
Description
Relocation function
Usage
f_relocate(x_pos_dominant, x_syn, radii_pos_dominant, p_of = 0)
Arguments
x_pos_dominant |
positive class dominant sample matrix or dataframe |
x_syn |
synthetically generated positive class sample matrix or dataframe |
radii_pos_dominant |
positive class dominant sample radii |
p_of |
proportion to increase cover radius. A real number between
|
Value
relocated data matrix
Author(s)
Fatih Saglam, saglamf89@gmail.com
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: |
proportion |
proportion of covered samples. A real number between |
p_of |
proportion to increase cover radius. A real number between
|
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, saglamf89@gmail.com
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")