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.
|Author||Andrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi|
|Maintainer||Andrea Dal Pozzolo <[email protected]>|
|License||GPL (>= 3)|
|Package repository||View on GitHub|
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