sym_cen_ln {noisemodel}

Symmetric center-based label noise

Description

Introduction of Symmetric center-based label noise into a classification dataset.

Usage

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

## S3 method for class 'formula'
sym_cen_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

Symmetric center-based label noise randomly selects (level·100)% of the samples in the dataset with independence of their class. The probability for chosing the noisy label is determined based on the distance between class centers. Thus, the mislabeling probability between classes increases as the distance between their centers decreases. This model is consistent with the intuition that samples in similar classes are more likely to be mislabeled. Besides, the model also allows mislabeling data in dissimilar classes with a relatively small probability, which corresponds to label noise caused by random errors.

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

X. Pu and C. Li. Probabilistic information-theoretic discriminant analysis for industrial label-noise fault diagnosis. IEEE Transactions on Industrial Informatics, 17(4):2664-2674, 2021. doi:10.1109/TII.2020.3001335.

See Also

glev_uni_ln, sym_hienc_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- sym_cen_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 <- sym_cen_ln(formula = Species ~ ., data = iris2D, level = 0.1)

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