Introduction of Boundary/dependent Gaussian attribute noise into a classification dataset.
# S3 method for default
boud_gau_an(x, y, level, k = 0.2, sortid = TRUE, ...)# S3 method for formula
boud_gau_an(formula, data, ...)
An object of class ndmodel with elements:
a data frame with the noisy input attributes.
a factor vector with the noisy output class.
an integer vector with the amount of noisy samples per attribute.
an integer vector list with the indices of noisy samples per attribute.
an integer vector with the amount of clean samples per attribute.
an integer vector list with the indices of clean samples per attribute.
an integer vector with the samples per class in the original data.
the full name of the noise introduction model used.
a list of the argument values.
the function call.
a data frame of input attributes.
a factor vector with the output class of each sample.
a double in [0,1] with the noise level to be introduced.
a double in [0,1] with the scale used for the standard deviation (default: 0.2).
a logical indicating if the indices must be sorted at the output (default: TRUE).
other options to pass to the function.
a formula with the output class and, at least, one input attribute.
a data frame in which to interpret the variables in the formula.
Boundary/dependent Gaussian attribute noise corrupts (level·100)% samples among the
((level+0.1)·100)% of samples closest to the decision boundary. Their attribute values are corrupted by adding a random number
that follows a Gaussian distribution of mean = 0 and standard deviation = (max-min)·k, being
max and min the limits of the attribute domain. For nominal attributes, a random value is chosen.
J. Bi and T. Zhang. Support vector classification with input data uncertainty. In Advances in Neural Information Processing Systems, volume 17, pages 161-168, 2004. url:https://proceedings.neurips.cc/paper/2004/hash/22b1f2e0983160db6f7bb9f62f4dbb39-Abstract.html.
imp_int_an, asy_int_an, print.ndmodel, summary.ndmodel, plot.ndmodel
# load the dataset
data(iris2D)
# usage of the default method
set.seed(9)
outdef <- boud_gau_an(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 <- boud_gau_an(formula = Species ~ ., data = iris2D, level = 0.1)
# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)
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