minp_uni_ln: Minority-proportional uniform label noise

In noisemodel: Noise Models for Classification Datasets

Minority-proportional uniform label noise

Description

Introduction of Minority-proportional uniform label noise into a classification dataset.

Usage

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

## S3 method for class 'formula'
minp_uni_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 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

Given a dataset, assume the original class distribution of class i is pi and the distribution of the minority class is pm. Let level be the noise level, Minority-proportional uniform label noise introduces noise proportionally to different classes, where a sample with its label i has a probability (pm/pi)·level to be corrupted as another random class. That is, the least common class is used as the baseline for noise introduction.

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

X. Zhu and X. Wu. Cost-guided class noise handling for effective cost-sensitive learning. In Proc. 4th IEEE International Conference on Data Mining, pages 297–304, 2004. doi: 10.1109/ICDM.2004.10108.

See Also

asy_uni_ln, maj_udir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

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