noisemodel (version 1.0.2)

unc_fixw_an: Unconditional fixed-width attribute noise

Description

Introduction of Unconditional fixed-width attribute noise into a classification dataset.

Usage

# S3 method for default
unc_fixw_an(x, y, level, k = 0.1, sortid = TRUE, ...)
# S3 method for formula
unc_fixw_an(formula, data, ...)

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.

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 in nominal attributes.

k

a double in [0,1] with the domain proportion of the noise width (default: 0.1).

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

Unconditional fixed-width attribute noise corrupts all the samples in the dataset. For each attribute A, all the original values are corrupted by adding a random number in the interval [-width, width], being width = (max(A)-min(A))·k. For nominal attributes, (level·100)% of the samples in the dataset are chosen and a random value is selected as noisy.

References

A. Ramdas, B. Poczos, A. Singh, and L. A. Wasserman. An analysis of active learning with uniform feature noise. In Proc. 17th International Conference on Artificial Intelligence and Statistics, volume 33 of JMLR, pages 805-813, 2014. url:http://proceedings.mlr.press/v33/ramdas14.html.

See Also

sym_end_an, sym_sgau_an, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

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

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