oned_uni_ln {noisemodel}

One-dimensional uniform label noise

Description

Introduction of One-dimensional uniform label noise into a classification dataset.

Usage

## Default S3 method:
oned_uni_ln(
  x,
  y,
  level,
  att,
  lower,
  upper,
  order = levels(y),
  sortid = TRUE,
  ...
)

## S3 method for class 'formula'
oned_uni_ln(formula, data, ...)

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.

att

an integer with the index of the attribute determining noisy samples.

lower

a vector with the lower bound to determine the noisy region of each class.

upper

a vector with the upper bound to determine the noisy region of 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

One-dimensional uniform label noise is based on the introduction of noise according to the values of the attribute att. Samples of class i with the attribute att falling between lower[i] and upper[i] have a probability level of being mislabeled. The labels of these samples are randomly replaced by other different ones within the set of class labels. The order of the class labels is determined by order.

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.

Note

Noise model adapted from the papers in References to multiclass data, considering a noise level to control the number of errors in the data

References

N. Gornitz, A. Porbadnigk, A. Binder, C. Sannelli, M. L. Braun, K. Muller, and M. Kloft. Learning and evaluation in presence of non-i.i.d. label noise. In Proc. 17th International Conference on Artificial Intelligence and Statistics, volume 33 of PMLR, pages 293–302, 2014. url:https://proceedings.mlr.press/v33/gornitz14.html.

See Also

attm_uni_ln, qua_uni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- oned_uni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                    level = 0.5, att = 1, lower = c(1.5,2,6), upper = c(2,4,7))

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

# usage of the method for class formula
set.seed(9)
outfrm <- oned_uni_ln(formula = Species ~ ., data = iris2D, 
                    level = 0.5, att = 1, lower = c(1.5,2,6), upper = c(2,4,7))

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