symd_uni_an {noisemodel} | R Documentation |
Introduction of Symmetric/dependent uniform attribute noise into a classification dataset.
## Default S3 method:
symd_uni_an(x, y, level, sortid = TRUE, ...)
## S3 method for class 'formula'
symd_uni_an(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/dependent uniform attribute noise corrupts (level
ยท100)% of the samples
in the dataset.
Their attribute values are replaced by random different ones between
the minimum and maximum of the domain of each attribute following a uniform distribution (for numerical
attributes) or choosing a random value (for nominal attributes).
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 attribute. |
idnoise |
an integer vector list with the indices of noisy samples per attribute. |
numclean |
an integer vector with the amount of clean samples per attribute. |
idclean |
an integer vector list with the indices of clean samples per attribute. |
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.
A. Petety, S. Tripathi, and N. Hemachandra. Attribute noise robust binary classification. In Proc. 34th AAAI Conference on Artificial Intelligence, pages 13897-13898, 2020.
sym_uni_an
, sym_cuni_an
, print.ndmodel
, summary.ndmodel
, plot.ndmodel
# load the dataset
data(iris2D)
# usage of the default method
set.seed(9)
outdef <- symd_uni_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 <- symd_uni_an(formula = Species ~ ., data = iris2D, level = 0.1)
# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)