unbalanced: Racing for Unbalanced Methods Selection

A dataset is said to be unbalanced when the class of interest (minority class) is much rarer than normal behaviour (majority class). The cost of missing a minority class is typically much higher that missing a majority class. Most learning systems are not prepared to cope with unbalanced data and several techniques have been proposed. This package implements some of most well-known techniques and propose a racing algorithm to select adaptively the most appropriate strategy for a given unbalanced task.

Version: 2.0
Depends: mlr, foreach, doParallel
Imports: FNN, RANN
Suggests: randomForest, ROCR
Published: 2015-06-26
Author: Andrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi
Maintainer: Andrea Dal Pozzolo <adalpozz at ulb.ac.be>
License: GPL (≥ 3)
URL: http://mlg.ulb.ac.be
NeedsCompilation: no
CRAN checks: unbalanced results


Reference manual: unbalanced.pdf


Package source: unbalanced_2.0.tar.gz
Windows binaries: r-devel: unbalanced_2.0.zip, r-release: unbalanced_2.0.zip, r-oldrel: unbalanced_2.0.zip
macOS binaries: r-release (arm64): unbalanced_2.0.tgz, r-release (x86_64): unbalanced_2.0.tgz, r-oldrel: unbalanced_2.0.tgz
Old sources: unbalanced archive

Reverse dependencies:

Reverse imports: alookr, hyperSMURF, themis


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