hubp_uni_ln {noisemodel}

Hubness-proportional uniform label noise

Description

Introduction of Hubness-proportional uniform label noise into a classification dataset.

Usage

## Default S3 method:
hubp_uni_ln(x, y, level, k = 3, sortid = TRUE, ...)

## S3 method for class 'formula'
hubp_uni_ln(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

an integer with the number of neighbors to compute the hubness of each sample (default: 3).

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

Hubness-proportional uniform label noise is based on the presence of hubs in the dataset. It selects (level·100)% of the samples in the dataset using a discrete probability distribution based on the concept of hubness, which is computed using the nearest neighbors of each sample. Then, the class labels of these samples are randomly replaced by different ones from the c classes.

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 class.

idnoise

an integer vector list with the indices of noisy samples.

numclean

an integer vector with the amount of clean samples per class.

idclean

an integer vector list with the indices of clean samples.

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

N. Tomasev and K. Buza. Hubness-aware kNN classification of high-dimensional data in presence of label noise. Neurocomputing, 160:157-172, 2015. doi:10.1016/j.neucom.2014.10.084.

See Also

smu_cuni_ln, oned_uni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

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

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