noisemodel (version 1.0.2)

pai_bdir_ln: Pairwise bidirectional label noise

Description

Introduction of Pairwise bidirectional label noise into a classification dataset.

Usage

# S3 method for default
pai_bdir_ln(x, y, level, pairs, order = levels(y), sortid = TRUE, ...)
# S3 method for formula
pai_bdir_ln(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 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.

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.

pairs

a list of integer vectors with the indices of classes to corrupt.

order

a character vector indicating the order of the classes (default: levels(y)).

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 vector (c1, c2) in pairs, Pairwise bidirectional label noise randomly selects (level·100)% of the samples from class c1 in the dataset and (level·100)% of the samples from class c2. Then, c1 samples are mislabeled as belonging to c2 and c2 samples are mislabeled as belonging to c1. The order of the class labels is determined by order.

References

S. Fefilatyev, M. Shreve, K. Kramer, L. O. Hall, D. B. Goldgof, R. Kasturi, K. Daly, A. Remsen, and H. Bunke. Label-noise reduction with support vector machines. In Proc. 21st International Conference on Pattern Recognition, pages 3504-3508, 2012. url:https://ieeexplore.ieee.org/document/6460920/.

See Also

print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# create new class with some samples
class <- as.character(iris2D$Species)
class[iris2D$Petal.Length > 6] <- "newclass"
iris2D$Species <- as.factor(class)

# usage of the default method
set.seed(9)
outdef <- pai_bdir_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                      level = 0.1, pairs = list(c(1,2), c(3,4)), 
                      order = c("virginica", "setosa", "newclass", "versicolor"))

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

# usage of the method for class formula
set.seed(9)
outfrm <- pai_bdir_ln(formula = Species ~ ., data = iris2D, 
                      level = 0.1, pairs = list(c(1,2), c(3,4)), 
                      order = c("virginica", "setosa", "newclass", "versicolor"))

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