Ensembled-based filters that use C4.5 classifier to remove label noise from a dataset as a preprocessing step of classification. For more information, see 'Details' and 'References' sections.
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## S3 method for class 'formula' C45robustFilter(formula, data, ...) ## Default S3 method: C45robustFilter(x, classColumn = ncol(x), ...) ## S3 method for class 'formula' C45votingFilter(formula, data, ...) ## Default S3 method: C45votingFilter(x, nfolds = 10, consensus = FALSE, classColumn = ncol(x), ...) ## S3 method for class 'formula' C45iteratedVotingFilter(formula, data, ...) ## Default S3 method: C45iteratedVotingFilter(x, nfolds = 10, consensus = FALSE, classColumn = ncol(x), ...)
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.
Number of folds in which the dataset is split.
Logical. If TRUE, consensus voting scheme is used. If FALSE, majority voting scheme is applied.
Full description of the methods can be looked up in the provided reference. Notice that C4.5 is used as base classifier instead of TILDE, since a standard attribute-value classification framework is considered (instead of the ILP classification approach of the reference).
C45robustFilter builds a C4.5 decision tree from the training data, and then
removes those instances misclassfied by this tree. The process is repeated until no instances are removed.
C45votingFilter splits the dataset into
nfolds folds, building and testing a C4.5 tree on every
nfolds-1 folds. Thus
nfolds-1 votes are gathered
for each instance. Removal is carried out by majority or consensus voting schemes.
C45iteratedVotingFilter somehow combines the two previous filter, since
C45votingFilter until no more noisy instances are removed.
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.
By means of a message, the number of noisy instances removed is displayed in the console.
Verbaeten S. (2002, December): Identifying mislabeled training examples in ILP classification problems, in Proc. 12th Belgian-Dutch Conf. Mach. Learn., Utrecht, The Netherlands, pp. 71-78.
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# Next example is not run in order to save time ## Not run: data(iris) out1 <- C45robustFilter(Species~.-Sepal.Length, iris) # We fix a seed since next two functions create partitions of data for the ensemble set.seed(1) out2 <- C45votingFilter(iris, consensus = TRUE) out3 <- C45iteratedVotingFilter(Species~., iris, nfolds = 5) print(out1) print(out2) print(out3) identical(out1$cleanData,iris[setdiff(1:nrow(iris),out1$remIdx),]) identical(out2$cleanData,iris[setdiff(1:nrow(iris),out2$remIdx),]) identical(out3$cleanData,iris[setdiff(1:nrow(iris),out3$remIdx),]) ## End(Not run)