dalpozz/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.

Package details

AuthorAndrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi
MaintainerAndrea Dal Pozzolo <[email protected]>
LicenseGPL (>= 3)
URL http://mlg.ulb.ac.be
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
dalpozz/unbalanced documentation built on Sept. 5, 2017, 12:32 a.m.