discCNN {rgnoisefilt}

Condensed Nearest Neighbors for Regression by Discretization

Description

Application of the discCNN noise filtering method in a regression dataset.

Usage

## Default S3 method:
discCNN(x, y, ...)

## S3 method for class 'formula'
discCNN(formula, data, ...)

Arguments

x

a data frame of input attributes.

y

a double vector with the output regressand of each sample.

...

other options to pass to the function.

formula

a formula with the output regressand and, at least, one input attribute.

data

a data frame in which to interpret the variables in the formula.

Details

discCNN discretizes the numerical output variable to make it compatible with Condensed Nearest Neighbors (CNN), typically used in classification tasks. CNN performs a first classification and stores all the samples that are misclassified. Then, those stored samples are taken as a training set. The process stops when all the unstored samples are correctly classified.

Value

The result of applying the regression filter is a reduced dataset containing the clean samples (without errors or noise), since it removes noisy samples (those with errors). This function returns an object of class rfdata, which contains information related to the noise filtering process in the form of a list with the following elements:

xclean

a data frame with the input attributes of clean samples (without errors).

yclean

a double vector with the output regressand of clean samples (without errors).

numclean

an integer with the amount of clean samples.

idclean

an integer vector with the indices of clean samples.

xnoise

a data frame with the input attributes of noisy samples (with errors).

ynoise

a double vector with the output regressand of noisy samples (with errors).

numnoise

an integer with the amount of noisy samples.

idnoise

an integer vector with the indices of noisy samples.

filter

the full name of the noise filter used.

param

a list of the argument values.

call

the function call.

Note that objects of the class rfdata support print.rfdata, summary.rfdata and plot.rfdata methods.

References

L. Devroye, L. Gyorfi and G. Lugosi, Condensed and edited nearest neighbor rules. In: A Probabilistic Theory of Pattern Recognition, 31:303-313, 1996. doi:10.1007/978-1-4612-0711-5_19.

A. Arnaiz-González, J. Díez-Pastor, J. Rodríguez, C. García-Osorio, Instance selection for regression by discretization. Expert Systems with Applications, 54:340-350, 2016. doi:10.1016/j.eswa.2015.12.046.

See Also

discENN, discTL, discNCL, print.rfdata, summary.rfdata

Examples

# load the dataset
data(rock)

# usage of the default method
set.seed(9)
out.def <- discCNN(x = rock[,-ncol(rock)], y = rock[,ncol(rock)])

# show results
summary(out.def, showid = TRUE)

# usage of the method for class formula
set.seed(9)
out.frm <- discCNN(formula = perm ~ ., data = rock)

# check the match of noisy indices
all(out.def$idnoise == out.frm$idnoise)