Package: hyperSMURF
Type: Package
Title: Hyper-Ensemble Smote Undersampled Random Forests
Version: 1.0
Date: 2016-08-08
Author: Giorgio Valentini [aut, cre] - AnacletoLab, Dipartimento di Informatica, Universita' degli Studi di Milano;
	Max Schubach [ctb] - Charite, Universitatsmedizin Berlin;
	Matteo Re [ctb] - AnacletoLab, Dipartimento di Informatica, Universita' degli Studi di Milano;
	Peter N Robinson [ctb] - The Jackson Laboratory for Genomic Medicine, Farmington CT, USA.
Maintainer: Giorgio Valentini <valentini@di.unimi.it>
Description: Machine learning supervised method to learn rare genomic features in imbalanced genetic data sets. This method can be also applied to classify or rank examples characterized by a high imbalance between the minority and majority class. hyperSMURF adopts a hyper-ensemble (ensemble of ensembles) approach, undersampling of the majority class and oversampling of the minority class to learn highly imbalanced data. Both single-core and parallel multi-core version of hyperSMURF are implemented.
License: GPL (>= 2)
LazyLoad: yes
Imports: mlr, BBmisc, ParamHelpers, unbalanced, randomForest, foreach,
        iterators, doParallel, parallel
NeedsCompilation: no
Packaged: 2016-08-11 16:58:15 UTC; valenti
Repository: CRAN
Date/Publication: 2016-08-11 20:53:34
