NoiseFiltersR (version 0.1.0)

EF: Ensemble Filter

Description

Ensemble-based filter for removing label noise from a dataset as a preprocessing step of classification. For more information, see 'Details' and 'References' sections.

Usage

"EF"(formula, data, ...)
"EF"(x, nfolds = 4, consensus = TRUE, 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.

nfolds

number of folds in which the dataset is split.

consensus

logical. If TRUE, consensus voting scheme is used. If FALSE, majority voting scheme is applied.

classColumn

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

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.

Details

Full description of the method can be looked up in the provided references. Dataset is split in nfolds folds, an ensemble of three different base classifiers (C4.5, 1-KNN, LDA) is built over every combination of nfolds-1 folds, and then tested on the other one. Finally, consensus or majority voting scheme is applied to remove noisy instances.

References

Brodley C. E., Friedl M. A. (1996, May): Improving automated land cover mapping by identifying and eliminating mislabeled observations from training data. In Geoscience and Remote Sensing Symposium, 1996. IGARSS'96.'Remote Sensing for a Sustainable Future.', International (Vol. 2, pp. 1379-1381). IEEE.

Brodley C. E., Friedl M. A. (1996, August): Identifying and eliminating mislabeled training instances. In AAAI/IAAI, Vol. 1 (pp. 799-805).

Brodley C. E., Friedl M. A. (1999): Identifying mislabeled training data. Journal of Artificial Intelligence Research, 131-167.

Examples

data(iris)
# We fix a seed since there exists a random partition for the ensemble
set.seed(1)
out <- EF(Species~., data = iris, consensus = FALSE)
summary(out, explicit = TRUE)
identical(out$cleanData, iris[setdiff(1:nrow(iris),out$remIdx),])