View source: R/009_gam_bor_ln.R
gam_bor_ln | R Documentation |
Introduction of Gamma borderline label noise into a classification dataset.
## Default S3 method: gam_bor_ln(x, y, level, shape = 1, rate = 0.5, k = 1, sortid = TRUE, ...) ## S3 method for class 'formula' gam_bor_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. |
shape |
a double with the shape for the gamma distribution (default: 1) |
rate |
a double with the rate for the gamma distribution (default: 0.5). |
k |
an integer with the number of nearest neighbors to be used (default: 1). |
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. |
Gamma borderline label noise uses an SVM to induce the decision border
in the dataset. For each sample, its distance
to the decision border is computed.
Then, a gamma distribution with parameters (shape
, rate
) is used to compute the
value for the probability density function associated to each distance.
Finally, (level
·100)% of the samples in the dataset are randomly selected to be mislabeled
according to their values of the probability density function. For each noisy sample, the
majority class among its k
-nearest neighbors of a different class
is chosen as the new label.
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, considering SVM with linear kernel as classifier, a mislabeling process using the neighborhood of noisy samples and a noise level to control the number of errors in the data.
J. Bootkrajang. A generalised label noise model for classification. In Proc. 23rd European Symposium on Artificial Neural Networks, pages 349-354, 2015. url:https://dblp.org/rec/conf/esann/Bootkrajang15.html?view=bibtex.
exp_bor_ln
, pmd_con_ln
, print.ndmodel
, summary.ndmodel
, plot.ndmodel
# load the dataset data(iris2D) # usage of the default method set.seed(9) outdef <- gam_bor_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 <- gam_bor_ln(formula = Species ~ ., data = iris2D, level = 0.1) # check the match of noisy indices identical(outdef$idnoise, outfrm$idnoise)
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