noisemodel (version 1.0.2)

smu_cuni_ln: Smudge-based completely-uniform label noise

Description

Introduction of Smudge-based completely-uniform label noise into a classification dataset.

Usage

# S3 method for default
smu_cuni_ln(x, y, level, sortid = TRUE, ...)
# S3 method for formula
smu_cuni_ln(formula, data, ...)

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.

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

Smudge-based completely-uniform label noise randomly selects (level·100)% of the samples in the dataset with independence of their class. Then, the labels of these samples are randomly replaced by others within the set of class labels. An additional attribute smudge is included in the dataset with value equal to 1 in mislabeled samples and equal to 0 in clean samples.

References

S. Thulasidasan, T. Bhattacharya, J. A. Bilmes, G. Chennupati, and J. Mohd-Yusof. Combating label noise in deep learning using abstention. In Proc. 36th International Conference on Machine Learning, volume 97 of PMLR, pages 6234-6243, 2019. url:http://proceedings.mlr.press/v97/thulasidasan19a.html.

See Also

oned_uni_ln, attm_uni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- smu_cuni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

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

# usage of the method for class formula
set.seed(9)
outfrm <- smu_cuni_ln(formula = Species ~ ., data = iris2D, level = 0.1)

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