View source: R/058_imp_int_an.R
imp_int_an | R Documentation |
Introduction of Importance interval-based attribute noise into a classification dataset.
## Default S3 method: imp_int_an(x, y, level, nbins = 10, ascending = TRUE, sortid = TRUE, ...) ## S3 method for class 'formula' imp_int_an(formula, data, ...)
x |
a data frame of input attributes. |
y |
a factor vector with the output class of each sample. |
level |
a double vector with the noise levels in [0,1] to be introduced into each attribute. |
nbins |
an integer with the number of bins to create (default: 10). |
ascending |
a boolean indicating how noise levels are assigned to attributes:
|
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. |
The values in level
are ordered and assigned to attributes according to their information gain (using the
ordering given by ascending
). Then,
Importance interval-based attribute noise corrupts (level
[i]·100)% of the values for
each attribute A[i] in the dataset. In order to corrupt each attribute A[i], (level
[i]·100)% of the
samples in the dataset are chosen. To corrupt a value in numeric
attributes, the attribute is split into equal-frequency intervals, one of its closest
intervals is picked out and a random value within the interval
is chosen as noisy. For nominal attributes, a random value within the domain is chosen.
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.
M. V. Mannino, Y. Yang, and Y. Ryu. Classification algorithm sensitivity to training data with non representative attribute noise. Decision Support Systems, 46(3):743-751, 2009. doi: 10.1016/j.dss.2008.11.021.
asy_int_an
, asy_uni_an
, print.ndmodel
, summary.ndmodel
, plot.ndmodel
# load the dataset data(iris2D) # usage of the default method set.seed(9) outdef <- imp_int_an(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = c(0.1, 0.2)) # show results summary(outdef, showid = TRUE) plot(outdef) # usage of the method for class formula set.seed(9) outfrm <- imp_int_an(formula = Species ~ ., data = iris2D, level = c(0.1, 0.2)) # check the match of noisy indices identical(outdef$idnoise, outfrm$idnoise)
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