RWO {SMOTEWB} | R Documentation |
Random Walk Oversampling (SMOTE)
Description
Resampling with RWO
Usage
RWO(x, y)
Arguments
x |
feature matrix. |
y |
a factor class variable with two classes. |
Details
RWO (Zhang and Li, 2014) is an oversampling method which generates data using variable standard error in a way that it preserves the variances of all variables.
Can work with classes more than 2.
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. |
Author(s)
Fatih Saglam, saglamf89@gmail.com
References
Zhang, H., & Li, M. (2014). RWO-Sampling: A random walk over-sampling approach to imbalanced data classification. Information Fusion, 20, 99-116.
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 <- RWO(x = x, y = y)
plot(m$x_new, col = m$y_new)
[Package SMOTEWB version 1.2.0 Index]