hyperSMURF: Hyper-Ensemble Smote Undersampled Random Forests

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.

Version: 2.0
Imports: unbalanced, randomForest
Published: 2018-04-29
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 at di.unimi.it>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: hyperSMURF results


Reference manual: hyperSMURF.pdf


Package source: hyperSMURF_2.0.tar.gz
Windows binaries: r-devel: hyperSMURF_2.0.zip, r-release: hyperSMURF_2.0.zip, r-oldrel: hyperSMURF_2.0.zip
macOS binaries: r-release (arm64): hyperSMURF_2.0.tgz, r-oldrel (arm64): hyperSMURF_2.0.tgz, r-release (x86_64): hyperSMURF_2.0.tgz, r-oldrel (x86_64): hyperSMURF_2.0.tgz
Old sources: hyperSMURF archive


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