View source: R/004_attm_uni_ln.R
attm_uni_ln | R Documentation |
Introduction of Attribute-mean uniform label noise into a classification dataset.
## Default S3 method: attm_uni_ln(x, y, level, sortid = TRUE, ...) ## S3 method for class 'formula' attm_uni_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. |
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.
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
B. Nicholson, V. S. Sheng, and J. Zhang. Label noise correction and application in crowdsourcing. Expert Systems with Applications, 66:149-162, 2016. doi: 10.1016/j.eswa.2016.09.003.
qua_uni_ln
, exps_cuni_ln
, print.ndmodel
, summary.ndmodel
, plot.ndmodel
# 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)
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