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Synthetic Minority Based on Probabilistic Distribution (SyMProD, 2020)

SyMProD.Rd

Resampling with SyMProD.

Usage

SyMProD(x, y, NT = 3, CT = 0, K = 5, M = 5, ovRate = 1, ...)

Arguments

x

feature matrix or dataframe.

y

a factor class variable. Can have more than two classes.

NT

z-score threshold of the noise removal. Samples with an absolute z-score above NT in any feature are removed from the calculations. Default is 3.

CT

threshold of the selection: a sample is used if its density in the class is larger than CT times its density of the other classes. Default is 0.

K

number of neighbours of the density calculation. Default is 5.

M

number of neighbours that are combined with the selected base sample. Default is 5.

ovRate

Oversampling rate. Class \(j\) receives round(ovRate * (n_max - n_j)) synthetic samples, where n_max is the size of the largest class (halves are rounded up). Default is 1 meaning full balance. A vector with one value per class is also accepted.

...

not used.

Value

a list with resampled dataset.

x_new

Resampled feature matrix.

y_new

Resampled target variable.

x_syn

Generated synthetic feature data.

y_syn

Generated synthetic label data.

Details

Year: 2020.

After noise removal, each sample of the class gets a closeness factor and a density from its neighbours in the class and in the other classes. Base samples are selected with probabilities proportional to their density ratio and replaced by a random convex combination of the base sample and its M nearest neighbours. If too few samples remain, random oversampling is used with a warning.

The method is defined for two classes. With more than two classes every class is oversampled against the union of the other classes (one-versus-rest).

References

Kunakorntum, I., Hinthong, W., & Phunchongharn, P. (2020). A synthetic minority based on probabilistic distribution (SyMProD) oversampling for imbalanced datasets. IEEE Access, 8, 114692-114704.

Author

Fatih Saglam, saglamf89@gmail.com

Examples


set.seed(1)
x <- rbind(matrix(rnorm(600, 3, 1), ncol = 2),
           matrix(rnorm(60, 5, 1), ncol = 2))
y <- as.factor(c(rep("negative", 300), rep("positive", 30)))

m <- SyMProD(x = x, y = y)
table(m$y_new)
#> 
#> negative positive 
#>      300      300