View source: R/017_maj_udir_ln.R
maj_udir_ln | R Documentation |
Introduction of Majority-class unidirectional label noise into a classification dataset.
## Default S3 method: maj_udir_ln(x, y, level, sortid = TRUE, ...) ## S3 method for class 'formula' maj_udir_ln(formula, data, ...)
x |
a data frame of input attributes. |
y |
a factor vector with the output class of each sample. |
level |
a double in [0,1] with the noise level to be introduced. |
sortid |
a logical indicating if the indices must be sorted at the output (default: |
... |
other options to pass to the function. |
formula |
a formula with the output class and, at least, one input attribute. |
data |
a data frame in which to interpret the variables in the formula. |
Let A be the majority class and B be the second majority class in the dataset.
The Majority-class unidirectional label noise introduction model randomly selects (level
·100)% of the samples
of A and labels them as B.
An object of class ndmodel
with elements:
xnoise |
a data frame with the noisy input attributes. |
ynoise |
a factor vector with the noisy output class. |
numnoise |
an integer vector with the amount of noisy samples per class. |
idnoise |
an integer vector list with the indices of noisy samples. |
numclean |
an integer vector with the amount of clean samples per class. |
idclean |
an integer vector list with the indices of clean samples. |
distr |
an integer vector with the samples per class in the original data. |
model |
the full name of the noise introduction model used. |
param |
a list of the argument values. |
call |
the function call. |
Noise model adapted from the papers in References to multiclass data.
J. Li, Q. Zhu, Q. Wu, Z. Zhang, Y. Gong, Z. He, and F. Zhu. SMOTE- NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution. Knowledge-Based Systems, 223:107056, 2021. doi: 10.1016/j.knosys.2021.107056.
asy_def_ln
, print.ndmodel
, summary.ndmodel
, plot.ndmodel
# load the dataset data(iris2D) # usage of the default method set.seed(9) outdef <- maj_udir_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1) # show results summary(outdef, showid = TRUE) plot(outdef) # usage of the method for class formula set.seed(9) outfrm <- maj_udir_ln(formula = Species ~ ., data = iris2D, level = 0.1) # check the match of noisy indices identical(outdef$idnoise, outfrm$idnoise)
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