sym_ddef_ln {noisemodel} | R Documentation |
Symmetric double-default label noise
Description
Introduction of Symmetric double-default label noise into a classification dataset.
Usage
## Default S3 method:
sym_ddef_ln(
x,
y,
level,
def1 = 1,
def2 = 2,
order = levels(y),
sortid = TRUE,
...
)
## S3 method for class 'formula'
sym_ddef_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. |
def1 |
an integer with the index of the first default class (default: 1). |
def2 |
an integer with the index of the second default class (default: 2). |
order |
a character vector indicating the order of the classes (default: |
sortid |
a logical indicating if the indices must be sorted at the output (default: |
... |
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
Symmetric double-default label noise randomly selects (level
ยท100)% of the samples
in the dataset with independence of their class. Then, the labels of these samples are
replaced by one of two fixed labels (def1
or def2
) within the set of class labels. The indices
def1
and def2
are taken according to the order given 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.
References
B. Han, J. Yao, G. Niu, M. Zhou, I. W. Tsang, Y. Zhang, and M. Sugiyama. Masking: A new perspective of noisy supervision. In Advances in Neural Information Processing Systems, volume 31, pages 5841-5851, 2018. url:https://proceedings.neurips.cc/paper/2018/hash/aee92f16efd522b9326c25cc3237ac15-Abstract.html.
See Also
sym_exc_ln
, sym_cuni_ln
, print.ndmodel
, summary.ndmodel
, plot.ndmodel
Examples
# load the dataset
data(iris2D)
# usage of the default method
set.seed(9)
outdef <- sym_ddef_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)],
level = 0.1, order = c("virginica", "setosa", "versicolor"))
# show results
summary(outdef, showid = TRUE)
plot(outdef)
# usage of the method for class formula
set.seed(9)
outfrm <- sym_ddef_ln(formula = Species ~ ., data = iris2D,
level = 0.1, order = c("virginica", "setosa", "versicolor"))
# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)