maj_udir_ln {noisemodel}

Majority-class unidirectional label noise

Description

Introduction of Majority-class unidirectional label noise into a classification dataset.

Usage

## Default S3 method:
maj_udir_ln(x, y, level, sortid = TRUE, ...)

## S3 method for class 'formula'
maj_udir_ln(formula, data, ...)

Arguments

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: TRUE).

...

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.

Details

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.

Value

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.

Note

Noise model adapted from the papers in References to multiclass data.

References

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.

See Also

asy_def_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# 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)