noisemodel (version 1.0.2)

mulc_udir_ln: Multiple-class unidirectional label noise

Description

Introduction of Multiple-class unidirectional label noise into a classification dataset.

Usage

# S3 method for default
mulc_udir_ln(x, y, level, goal, order = levels(y), sortid = TRUE, ...)
# S3 method for formula
mulc_udir_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.

goal

an integer vector with the indices of noisy classes for each class.

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

Multiple-class unidirectional label noise introduction model randomly selects (level·100)% of the samples of each class c with goal[c] != NA. Then, the labels c of these samples are replaced by the class indicated in goal[c]. The order of indices in goal is determined by order.

References

Q. Wang, B. Han, T. Liu, G. Niu, J. Yang, and C. Gong. Tackling instance-dependent label noise via a universal probabilistic model. In Proc. 35th AAAI Conference on Artificial Intelligence, pages 10183-10191, 2021. url:https://ojs.aaai.org/index.php/AAAI/article/view/17221.

See Also

minp_uni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- mulc_udir_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1, 
                        goal = c(NA, 1, 2), order = c("virginica", "setosa", "versicolor"))

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

# usage of the method for class formula
set.seed(9)
outfrm <- mulc_udir_ln(formula = Species ~ ., data = iris2D, level = 0.1, 
                        goal = c(NA, 1, 2), order = c("virginica", "setosa", "versicolor"))

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