sym_end_an {noisemodel}

Symmetric end-directed attribute noise

Description

Introduction of Symmetric end-directed attribute noise into a classification dataset.

Usage

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

## S3 method for class 'formula'
sym_end_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.

scale

a double in (0,1) with the scale to be used (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

For each attribute A, Symmetric end-directed attribute noise computes a value k = scale·max(A). Then, it chooses (level·100)% of the values of that attribute. For each value, it applies the following procedure:

• If the value is less than the median of the attribute, the value transforms into adding k to the maximum of the attribute A.

• If the value is greater than the median of the attribute, the value transforms into subtracting k from the minimum of the attribute A.

• If the value matches the median, one of the two previous alternatives 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

T. M. Khoshgoftaar and J. V. Hulse. Empirical case studies in attribute noise detection. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 39(4):379-388, 2009. doi:10.1109/TSMCC.2009.2013815.

See Also

sym_sgau_an, symd_gau_an, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

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

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