mind_bdir_ln {noisemodel}R Documentation

Minority-driven bidirectional label noise

Description

Introduction of Minority-driven bidirectional label noise into a classification dataset.

Usage

## Default S3 method:
mind_bdir_ln(x, y, level, pos = 0.1, sortid = TRUE, ...)

## S3 method for class 'formula'
mind_bdir_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.

pos

a double in [0,1] with the proportion of samples from the positive class (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

Minority-driven bidirectional label noise randomly selects n = 2m·level samples in the dataset (with m the number of samples in the minority class), making sure that n·pos samples belong to the minority class and the rest to the majority class. Then, minority class samples are mislabeled as belonging to the majority class and majority class samples are mislabeled as belonging to the minority class. In case of ties determining minority and majority classes, a random class is chosen among them.

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 to multiclass data.

References

A. Folleco, T. M. Khoshgoftaar, J. V. Hulse, and L. A. Bullard. Software quality modeling: The impact of class noise on the random forest classifier. In Proc. 2008 IEEE Congress on Evolutionary Computation, pages 3853–3859, 2008. doi:10.1109/CEC.2008.4631321.

See Also

fra_bdir_ln, irs_bdir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- mind_bdir_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.5)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- mind_bdir_ln(formula = Species ~ ., data = iris2D, level = 0.5)

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


[Package noisemodel version 1.0.2 Index]