ugau_bor_ln: Uneven-Gaussian borderline label noise

In noisemodel: Noise Models for Classification Datasets

Uneven-Gaussian borderline label noise

Description

Introduction of Uneven-Gaussian borderline label noise into a classification dataset.

Usage

## Default S3 method:
ugau_bor_ln(
  x,
  y,
  level,
  mean = 0,
  sd = 1,
  k = 1,
  order = levels(y),
  sortid = TRUE,
  ...
)

## S3 method for class 'formula'
ugau_bor_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 vector with the noise levels in [0,1] to be introduced into each class.
mean	a double with the mean for the Gaussian distribution (default: 0).
sd	a double with the standard deviation for the Gaussian distribution (default: 1).
k	an integer with the number of nearest neighbors to be used (default: 1).
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

Uneven-Gaussian borderline label noise uses an SVM to induce the decision border in the dataset. For each sample, its distance to the decision border is computed. Then, a Gaussian distribution with parameters (mean, sd) is used to compute the value for the probability density function associated to each distance. For each class c[i], it randomly selects (level[i]·100)% of the samples in the dataset based on their values of the probability density function -the order of the class labels is determined by order. For each noisy sample, the majority class among its k-nearest neighbors of a different class is chosen as the new label.

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 to multiclass data, considering SVM with linear kernel as classifier, a mislabeling process using the neighborhood of noisy samples and a noise level to control the number of errors in the data.

References

J. Du and Z. Cai. Modelling class noise with symmetric and asymmetric distributions. In Proc. 29th AAAI Conference on Artificial Intelligence, pages 2589-2595, 2015. url:https://dl.acm.org/doi/10.5555/2886521.2886681.

See Also

gaum_bor_ln, gau_bor_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- ugau_bor_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                      level = c(0.1, 0.2, 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 <- ugau_bor_ln(formula = Species ~ ., data = iris2D,
                      level = c(0.1, 0.2, 0.3), order = c("virginica", "setosa", "versicolor"))

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

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