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imbalance

imbalance provides a set of tools to work with imbalanced datasets: novel oversampling algorithms, filtering of instances and evaluation of synthetic instances.

Installation

You can install imbalance from Github with:

# install.packages("devtools")
devtools::install_github("ncordon/imbalance")

Examples

Run pdfos algorithm on newthyroid1 imbalanced dataset and plot a comparison between attributes.

library("imbalance")
data(newthyroid1)

newSamples <- pdfos(newthyroid1, numInstances = 80)
# Join new samples with old imbalanced dataset
newDataset <- rbind(newthyroid1, newSamples)
# Plot a visual comparison between both datasets
plotComparison(newthyroid1, newDataset, attrs = names(newthyroid1)[1:3], cols = 2, classAttr = "Class")

After filtering examples with neater:

filteredSamples <- neater(newthyroid1, newSamples, iterations = 500)
#> [1] "12 samples filtered by NEATER"
filteredNewDataset <- rbind(newthyroid1, filteredSamples)
plotComparison(newthyroid1, filteredNewDataset, attrs = names(newthyroid1)[1:3])

Execute method ADASYN using the wrapper provided by the package, comparing imbalance ratios of the dataset before and after oversampling:

imbalanceRatio(glass0)
#> [1] 0.4861111
newDataset <- oversample(glass0, method = "ADASYN")
imbalanceRatio(newDataset)
#> [1] 0.9722222

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.