asy_spa_ln {noisemodel}

Asymmetric sparse label noise

Description

Introduction of Asymmetric sparse label noise into a classification dataset.

Usage

## Default S3 method:
asy_spa_ln(x, y, levelO, levelE, order = levels(y), sortid = TRUE, ...)

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

Arguments

x

a data frame of input attributes.

y

a factor vector with the output class of each sample.

levelO

a double with the noise level in [0,1] to be introduced into each odd class.

levelE

a double with the noise level in [0,1] to be introduced into each even class.

order

a character vector indicating the order of the classes (default: levels(y)).

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

Asymmetric sparse label noise randomly selects (levelO·100)% of the samples in each odd class and (levelE·100)% of the samples in each even class -the order of the class labels is determined by order. Then, each odd class is flipped to the next class, whereas each even class is flipped to the previous class. If the dataset has an odd number of classes, the last class is not corrupted.

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.

References

J. Wei and Y. Liu. When optimizing f-divergence is robust with label noise. In Proc. 9th International Conference on Learning Representations, pages 1-11, 2021. url:https://openreview.net/forum?id=WesiCoRVQ15.

See Also

mind_bdir_ln, fra_bdir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- asy_spa_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                       levelO = 0.1, levelE = 0.3, order = c("virginica", "setosa", "versicolor"))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- asy_spa_ln(formula = Species ~ ., data = iris2D, 
                        levelO = 0.1, levelE = 0.3, order = c("virginica", "setosa", "versicolor"))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)