exps_cuni_ln: Exponential/smudge completely-uniform label noise

In noisemodel: Noise Models for Classification Datasets

Exponential/smudge completely-uniform label noise

Description

Introduction of Exponential/smudge completely-uniform label noise into a classification dataset.

Usage

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

## S3 method for class 'formula'
exps_cuni_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 lambda value.
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

Exponential/smudge completely-uniform label noise includes an additional attribute (smudge) in the dataset with random values in [0,1]. This attribute is used to compute the mislabeling probability for each sample based on an exponential function (in which level is used as lambda). It selects samples in the dataset based on these probabilities. Finally, the labels of these samples are randomly replaced by others within the set of class labels (this model can choose the original label of a sample as noisy).

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

B. Denham, R. Pears, and M. A. Naeem. Null-labelling: A generic approach for learning in the presence of class noise. In Proc. 20th IEEE International Conference on Data Mining, pages 990–995, 2020. doi: 10.1109/ICDM50108.2020.00114.

See Also

opes_idu_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- exps_cuni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.8)

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

# usage of the method for class formula
set.seed(9)
outfrm <- exps_cuni_ln(formula = Species ~ ., data = iris2D, level = 0.8)

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

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