noisemodel (version 1.0.2)

opes_idu_ln: Open-set ID/uniform label noise

Description

Introduction of Open-set ID/uniform label noise into a classification dataset.

Usage

# S3 method for default
opes_idu_ln(x, y, level, openset = c(1), order = levels(y), sortid = TRUE, ...)
# S3 method for formula
opes_idu_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 with the noise level in [0,1] to be introduced.

openset

an integer vector with the indices of classes in the open set (default: c(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

Open-set ID/uniform label noise corrupts (level·100)% of the samples with classes in openset. For each sample selected, a label from in-distribution classes is randomly chosen. The order of the class labels for the indices in openset is determined by order.

References

P. H. Seo, G. Kim, and B. Han. Combinatorial inference against label noise. In Advances in Neural Information Processing Systems, volume 32, pages 1171-1181, 2019. url:https://proceedings.neurips.cc/paper/2019/hash/0cb929eae7a499e50248a3a78f7acfc7-Abstract.html.

See Also

asy_spa_ln, mind_bdir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- opes_idu_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                     level = 0.4, order = c("virginica", "setosa", "versicolor"))

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

# usage of the method for class formula
set.seed(9)
outfrm <- opes_idu_ln(formula = Species ~ ., data = iris2D, 
                     level = 0.4, order = c("virginica", "setosa", "versicolor"))

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