unbalanced (2.0)

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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.

Maintainer: Andrea Dal Pozzolo
Author(s): Andrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi

License: GPL (>= 3)

Uses: doParallel, FNN, foreach, mlr, RANN, ROCR, randomForest

Released over 4 years ago.

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