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 |
Please use the canonical form https://CRAN.R-project.org/package=hyperSMURF to link to this page.