TomekLinks

Description

Similarity-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'
TomekLinks(formula, data, ...)

## Default S3 method:
TomekLinks(x, 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.

classColumn

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

Details

The function TomekLinks removes "TomekLink points" from the dataset. These are introduced in [Tomek, 1976], and are expected to lie on the border between classes. Removing such points is a typical procedure for cleaning noise [Lorena, 2002].

Since the computation of mean points is necessary for TomekLinks, only numeric attributes are allowed. Moreover, only two different classes are allowed to detect TomekLinks.

Value

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

  • cleanData is a data frame containing the filtered dataset.

  • remIdx is a vector of integers indicating the indexes for removed instances (i.e. their row number with respect to the original data frame).

  • repIdx is a vector of integers indicating the indexes for repaired/relabelled instances (i.e. their row number with respect to the original data frame).

  • repLab is a factor containing the new labels for repaired instances.

  • parameters is a list containing the argument values.

  • call contains the original call to the filter.

  • extraInf is a character that includes additional interesting information not covered by previous items.

References

Tomek I. (Nov. 1976): Two modifications of CNN, IEEE Trans. Syst., Man, Cybern., vol. 6, no. 11, pp. 769-772.

Lorena A. C., Batista G. E. A. P. A., de Carvalho A. C. P. L. F., Monard M. C. (Nov. 2002): The influence of noisy patterns in the performance of learning methods in the splice junction recognition problem, in Proc. 7th Brazilian Symp. Neural Netw., Recife, Brazil, pp. 31-37.

Examples

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# Next code fails since TomekLinks method is designed for two-class problems.
# Some decomposition strategy like OVO or OVA could be used to overcome this.
## Not run: 
data(iris)
out <- TomekLinks(Species~., data = iris)

## End(Not run)