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.

AuthorAndrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi
Date of publication2015-06-26 13:34:37
MaintainerAndrea Dal Pozzolo <adalpozz@ulb.ac.be>
LicenseGPL (>= 3)

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