sym_gau_an {noisemodel}

Symmetric Gaussian attribute noise

Description

Introduction of Symmetric Gaussian attribute noise into a classification dataset.

Usage

## Default S3 method:
sym_gau_an(x, y, level, k = 0.2, sortid = TRUE, ...)

## S3 method for class 'formula'
sym_gau_an(formula, data, ...)

Arguments

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.

k

a double in [0,1] with the scale used for the standard deviation (default: 0.2).

sortid

a logical indicating if the indices must be sorted at the output (default: TRUE).

...

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.

Details

Symmetric Gaussian attribute noise corrupts (level·100)% of the values of each attribute in the dataset. In order to corrupt an attribute A, (level·100)% of the samples in the dataset are chosen. Then, their values for A are corrupted adding a random value 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.

Value

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.

Note

Noise model adapted from the papers in References.

References

J. A. Sáez, M. Galar, J. Luengo, and F. Herrera. Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition. Knowledge and Information Systems, 38(1):179-206, 2014. doi:10.1007/s10115-012-0570-1.

See Also

sym_int_an, symd_uni_an, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- sym_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 <- sym_gau_an(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)