View source: R/046_sym_nean_ln.R
sym_nean_ln | R Documentation |
Introduction of Symmetric nearest-neighbor label noise into a classification dataset.
## Default S3 method: sym_nean_ln(x, y, level, sortid = TRUE, ...) ## S3 method for class 'formula' sym_nean_ln(formula, data, ...)
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: |
... |
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. |
Symmetric nearest-neighbor label noise randomly selects (level
·100)% of the samples
in the dataset with independence of their class. Then, the labels of these samples are replaced by
the label of the nearest sample of a different class.
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.
P. H. Seo, G. Kim, and B. Han. Combinatorial inference against label noise. In Advances in Neural Information Processing Systems, volume 32, pages 1171-1181, 2019. url:https://proceedings.neurips.cc/paper/2019/hash/0cb929eae7a499e50248a3a78f7acfc7-Abstract.html.
sym_con_ln
, sym_cen_ln
, print.ndmodel
, summary.ndmodel
, plot.ndmodel
# load the dataset data(iris2D) # usage of the default method set.seed(9) outdef <- sym_nean_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 <- sym_nean_ln(formula = Species ~ ., data = iris2D, level = 0.1) # check the match of noisy indices identical(outdef$idnoise, outfrm$idnoise)
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