symd_gimg_an: Symmetric/dependent Gaussian-image attribute noise

In noisemodel: Noise Models for Classification Datasets

Symmetric/dependent Gaussian-image attribute noise

Description

Introduction of Symmetric/dependent Gaussian-image attribute noise into a classification dataset.

Usage

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

## S3 method for class 'formula'
symd_gimg_an(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/dependent Gaussian-image attribute noise corrupts (level·100)% of the samples in the dataset. For each sample, a Gaussian distribution (with matching mean and variance to the original sample) is used to generate random attribute values for that sample.

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

L. Huang, C. Zhang, and H. Zhang. Self-adaptive training: Beyond empirical risk minimization. In Proceedings of the Advances in Neural Information Processing Systems, 2020, Vol. 33, pp. 19365–19376. https://proceedings.neurips.cc/paper/2020/file/e0ab531ec312161511493b002f9be2ee-Paper.pdf

See Also

unc_vgau_an, symd_rpix_an, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- symd_gimg_an(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 <- symd_gimg_an(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.