Description Usage Arguments Details Value References Examples
View source: R/EdgeBoostFilter.R
Ensemblebased filter for removing label noise from a dataset as a preprocessing step of classification. For more information, see 'Details' and 'References' sections.
1 2 3 4 5 6  ## S3 method for class 'formula'
edgeBoostFilter(formula, data, ...)
## Default S3 method:
edgeBoostFilter(x, m = 15, percent = 0.05,
threshold = 0, classColumn = ncol(x), ...)

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. 
m 
Number of boosting iterations 
percent 
Real number between 0 and 1. It sets the percentage of instances to be removed (as long as
their edge value exceeds the parameter 
threshold 
Real number between 0 and 1. It sets the minimum edge value required by an instance in order to be removed. 
classColumn 
Positive integer indicating the column which contains the (factor of) classes. By default, the last column is considered. 
The full description of the method can be looked up in the provided reference.
An AdaBoost scheme (Freund & Schapire) is applied with a default C4.5 tree as weak classifier.
After m
iterations, those instances with larger (according to the constraints
percent
and threshold
) edge values (Wheway, Freund & Schapire) are considered noisy
and thus removed.
Notice that making use of extreme values (i.e. percent=1
or threshold=0
) any
'removing constraints' can be ignored.
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.
Freund Y., Schapire R. E. (1997): A decisiontheoretic generalization of online learning and an application to boosting. Journal of computer and system sciences, 55(1), 119139.
Wheway V. (2001, January): Using boosting to detect noisy data. In Advances in Artificial Intelligence. PRICAI 2000 Workshop Reader (pp. 123130). Springer Berlin Heidelberg.
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