noisemodel (version 1.0.2)

fra_bdir_ln: Fraud bidirectional label noise

Description

Introduction of Fraud bidirectional label noise into a classification dataset.

Usage

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

Fraud bidirectional label noise randomly selects (level·100)% of the samples from the minority class in the dataset and level·10 samples from the majority class. Then, minority class samples are mislabeled as belonging to the majority class and majority class samples are mislabeled as belonging to the minority class. In case of ties determining minority and majority classes, a random class is chosen among them.

References

Z. Salekshahrezaee, J. L. Leevy, and T. M. Khoshgoftaar. A reconstruction error-based framework for label noise detection. Journal of Big Data, 8(1):1-16, 2021. tools:::Rd_expr_doi("10.1186/s40537-021-00447-5").

See Also

irs_bdir_ln, pai_bdir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

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

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