noisemodel (version 1.0.2)

sco_con_ln: Score-based confidence label noise

Description

Introduction of Score-based confidence label noise into a classification dataset.

Usage

# S3 method for default
sco_con_ln(x, y, level, sortid = TRUE, ...)
# S3 method for formula
sco_con_ln(formula, data, ...)

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.

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

Score-based confidence label noise follows the intuition that hard samples are more likely to be mislabeled. Given the confidence per class of each sample, if it is predicted with a different class with a high probability, it means that it is hard to clearly distinguish the sample from this class. The confidence information is used to compute a mislabeling score for each sample and its potential noisy label. Finally, (level·100)% of the samples with the highest mislabeling scores are chosen as noisy.

References

P. Chen, J. Ye, G. Chen, J. Zhao, and P. Heng. Beyond class-conditional assumption: A primary attempt to combat instance-dependent label noise. In Proc. 35th AAAI Conference on Artificial Intelligence, pages 11442-11450, 2021. url:https://ojs.aaai.org/index.php/AAAI/article/view/17363.

See Also

mis_pre_ln, smam_bor_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

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

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