Package: unbalanced
Type: Package
Title: The package implements different data-driven method for
        unbalanced datasets
Version: 1.0
Date: 2014-01-27
Author: Andrea Dal Pozzolo
Maintainer: Andrea Dal Pozzolo <dalpozz@gmail.com>
Description: 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. Proposed strategies essentially belong to the following categories: sampling and distance-based. Sampling techniques up-sample or down-sample a class of instances. SMOTE generates synthetic minority examples. Distance based techniques use distances between input points to under-sample or to remove noisy and borderline examples.
License: GPL (>= 2)
Depends: FNN, RANN
Packaged: 2014-01-27 11:46:58 UTC; Andrea
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-01-27 13:08:24
