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.


Reference manual

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2.0 by Andrea Dal Pozzolo, 6 years ago

Browse source code at

Authors: Andrea Dal Pozzolo , Olivier Caelen and Gianluca Bontempi

Documentation:   PDF Manual  

GPL (>= 3) license

Imports FNN, RANN

Depends on mlr, foreach, doParallel

Suggests randomForest, ROCR

Imported by alookr, hyperSMURF, themis.

See at CRAN