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

Documentation:

Reference manual:

unbalanced.pdf

Downloads:

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-oldrel (arm64): unbalanced_2.0.tgz, r-release (x86_64): unbalanced_2.0.tgz, r-oldrel (x86_64): unbalanced_2.0.tgz
Old sources:	unbalanced archive

Reverse dependencies:

Reverse imports:

alookr, hyperSMURF, themis

Linking:

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