View source: R/001_asy_def_ln.R
asy_def_ln | R Documentation |
Introduction of Asymmetric default label noise into a classification dataset.
## Default S3 method: asy_def_ln(x, y, level, def = 1, order = levels(y), sortid = TRUE, ...) ## S3 method for class 'formula' asy_def_ln(formula, data, ...)
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. |
def |
an integer with the index of the default class (default: 1). |
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. |
Asymmetric default label noise randomly selects (level
[i]·100)% of the samples
of each class C[i] in the dataset -the order of the class labels is determined by
order
. Then, the labels of these samples are
replaced by a fixed label (C[def
]) within the set of class labels.
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. |
Noise model adapted from the papers in References.
R. C. Prati, J. Luengo, and F. Herrera. Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise. Knowledge and Information Systems, 60(1):63–97, 2019. doi: 10.1007/s10115-018-1244-4.
sym_nean_ln
, print.ndmodel
, summary.ndmodel
, plot.ndmodel
# load the dataset data(iris2D) # usage of the default method set.seed(9) outdef <- asy_def_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = c(0.1, 0.2, 0.3), order = c("virginica", "setosa", "versicolor")) # show results summary(outdef, showid = TRUE) plot(outdef) # usage of the method for class formula set.seed(9) outfrm <- asy_def_ln(formula = Species ~ ., data = iris2D, level = c(0.1, 0.2, 0.3), order = c("virginica", "setosa", "versicolor")) # check the match of noisy indices identical(outdef$idnoise, outfrm$idnoise)
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