Blame Based Noise Reduction
Description
Similaritybased 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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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. 
k 
Number of nearest neighbors to be used. 
classColumn 
positive integer indicating the column which contains the (factor of) classes. By default, the last column is considered. 
Details
BBNR
removes an instance 'X' if: (i) it participates in the misclassification of other instance
(i.e. 'X' is among the k
nearest neighbors of a misclassified instance and has a different class);
and (ii) its removal does not produce a misclassification in instances that, initially, were correctly
classified by 'X' (i.e. 'X' was initially among the k
nearest neighbors and had the same class).
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
Delany S. J., Cunningham P. (2004): An analysis of casebase editing in a spam filtering system. In Advances in CaseBased Reasoning (pp. 128141). Springer Berlin Heidelberg.
Examples
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