CVCF: Cross-Validated Committees Filter

Description Usage Arguments Details Value References Examples

View source: R/CVCF.R

Description

Ensemble-based filter for removing label noise from a dataset as a preprocessing step of classification. For more information, see 'Details' and 'References' sections.

Usage

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

## Default S3 method:
CVCF(x, nfolds = 10, consensus = FALSE,
  classColumn = ncol(x), ...)

Arguments

formula

A formula describing the classification variable and the attributes to be used.

data, x

data frame containing the tranining dataset to be filtered.

...

Optional parameters to be passed to other methods.

nfolds

number of folds in which the dataset is split.

consensus

logical. If TRUE, consensus voting scheme is used. If FALSE, majority voting scheme is applied.

classColumn

positive integer indicating the column which contains the (factor of) classes. By default, the last column is considered.

Details

Full description of the method can be looked up in the provided references. Dataset is split in nfolds folds, a base classifiers (C4.5 in this implementation) is built over every combination of nfolds-1 folds, and then tested on the whole dataset. Finally, consensus or majority voting scheme is applied to remove noisy instances.

Value

An object of class filter, which is a list with seven components:

References

Verbaeten S., Van Assche A. (2003, June): Ensemble methods for noise elimination in classification problems. Proc. 4th Int. Conf. Multiple Classifier Syst., Guildford, U.K., pp. 317-325.

Examples

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# Next example is not run in order to save time
## Not run: 
data(iris)
# We fix a seed since there exists a random partition for the ensemble
set.seed(1)
out <- CVCF(Species~.-Sepal.Width, data = iris)
print(out)
identical(out$cleanData, iris[setdiff(1:nrow(iris),out$remIdx),])

## End(Not run)

NoiseFiltersR documentation built on May 29, 2017, 3:12 p.m.

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