uncs_guni_cn: Unconditional/symmetric Gaussian/uniform combined noise

In noisemodel: Noise Models for Classification Datasets

Unconditional/symmetric Gaussian/uniform combined noise

Description

Introduction of Unconditional/symmetric Gaussian/uniform combined noise into a classification dataset.

Usage

## Default S3 method:
uncs_guni_cn(x, y, level, k = 0.2, sortid = TRUE, ...)

## S3 method for class 'formula'
uncs_guni_cn(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.
k	a double in [0,1] with the scale used for the standard deviation (default: 0.2).
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

Unconditional/symmetric Gaussian/uniform combined noise corrupts all the samples for each attribute in the dataset. Their values are corrupted by adding a random value following a Gaussian distribution of mean = 0 and standard deviation = (max-min)·k, being max and min the limits of the attribute domain. For nominal attributes, a random value is chosen. Additionally, this noise model also selects (level·100)% of the samples in the dataset with independence of their class. The labels of these samples are randomly replaced by different ones within the set of class labels.

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 variable.
idnoise	an integer vector list with the indices of noisy samples per variable.
numclean	an integer vector with the amount of clean samples per variable.
idclean	an integer vector list with the indices of clean samples per variable.
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

S. Kazmierczak and J. Mandziuk. A committee of convolutional neural networks for image classification in the concurrent presence of feature and label noise. In Proc. 16th International Conference on Parallel Problem Solving from Nature, volume 12269 of LNCS, pages 498-511, 2020. doi: 10.1007/978-3-030-58112-1_34.

See Also

sym_cuni_cn, sym_cuni_an, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

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

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

noisemodel documentation built on Oct. 17, 2022, 9:05 a.m.