The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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.
Health stats visible at Monitor.