Similarity-based filter for removing label noise from a dataset as a preprocessing step of classification. For more information, see 'Details' and 'References' sections.
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A formula describing the classification variable and the attributes to be used.
Data frame containing the tranining dataset to be filtered.
Optional parameters to be passed to other methods.
positive integer indicating the column which contains the (factor of) classes. By default, the last column is considered.
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.
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.
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.
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