noisemodel (version 1.0.2)

attm_uni_ln: Attribute-mean uniform label noise

Description

Introduction of Attribute-mean uniform label noise into a classification dataset.

Usage

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

For each sample, its distance to the mean of each attribute is computed. Then, (level·100)% of the samples in the dataset are randomly selected to be mislabeled, more likely choosing samples whose features are generally close to the mean. The labels of these samples are randomly replaced by other different ones within the set of class labels.

References

B. Nicholson, V. S. Sheng, and J. Zhang. Label noise correction and application in crowdsourcing. Expert Systems with Applications, 66:149-162, 2016. tools:::Rd_expr_doi("10.1016/j.eswa.2016.09.003").

See Also

qua_uni_ln, exps_cuni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

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

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