noisemodel (version 1.0.2)

sigb_uni_ln: Sigmoid-bounded uniform label noise

Description

Introduction of Sigmoid-bounded uniform label noise into a classification dataset.

Usage

# S3 method for default
sigb_uni_ln(x, y, level, order = levels(y), sortid = TRUE, ...)
# S3 method for formula
sigb_uni_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 vector with the noise levels in [0,1] to be introduced into 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

Sigmoid-bounded uniform label noise generates bounded instance-dependent and label-dependent label noise at random using a weight for each sample in the dataset to compute its noise probability through a sigmoid function. Note that this noise model considers the maximum noise level per class given by level, so the current noise level in each class may be lower than that specified. The order of the class labels is determined by order.

References

J. Cheng, T. Liu, K. Ramamohanarao, and D. Tao. Learning with bounded instance and label-dependent label noise. In Proc. 37th International Conference on Machine Learning, volume 119 of PMLR, pages 1789-1799, 2020. url:http://proceedings.mlr.press/v119/cheng20c.html.

See Also

larm_uni_ln, hubp_uni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- sigb_uni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                      level = c(0.1, 0.2, 0.3))

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

# usage of the method for class formula
set.seed(9)
outfrm <- sigb_uni_ln(formula = Species ~ ., data = iris2D, 
                      level = c(0.1, 0.2, 0.3))

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