NoiseFiltersR (version 0.1.0)

ENN: Edited Nearest Neighbors

Description

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

Usage

"ENN"(formula, data, ...)
"ENN"(x, k = 3, 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.

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.

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

ENN finds the k nearest neighbors for each instance, which is removed if the majority class in this neighborhood is different from its class.

References

Wilson D. L. (1972): Asymptotic properties of nearest neighbor rules using edited data. Systems, Man and Cybernetics, IEEE Transactions on, (3), 408-421.

Examples

data(iris)
out <- ENN(Species~., data = iris, k = 5)
summary(out)
identical(out$cleanData, iris[setdiff(1:nrow(iris),out$remIdx),])