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.
| Version: | 1.1.3 |
| Imports: | unbalanced, randomForest, foreach, iterators, doParallel, parallel |
| Published: | 2018-02-04 |
| 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 |
| CRAN checks: | hyperSMURF results |
| Reference manual: | hyperSMURF.pdf |
| Package source: | hyperSMURF_1.1.3.tar.gz |
| Windows binaries: | r-devel: hyperSMURF_1.1.3.zip, r-release: hyperSMURF_1.1.3.zip, r-oldrel: hyperSMURF_1.1.3.zip |
| OS X El Capitan binaries: | r-release: hyperSMURF_1.1.3.tgz |
| OS X Mavericks binaries: | r-oldrel: hyperSMURF_1.1.2.tgz |
| Old sources: | hyperSMURF archive |
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