An implementation of feature selection, weighting and ranking via simultaneous perturbation stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using some error measures such as mean squared error (for regression problems) and accuracy rate (for classification problems).
| Version: | 2.0.4 |
| Depends: | mlr3 (≥ 0.14.0),future (≥ 1.28.0),tictoc (≥ 1.0) |
| Imports: | mlr3pipelines (≥ 0.4.2),mlr3learners (≥ 0.5.4),ranger (≥0.14.1), parallel (≥ 3.4.2),ggplot2 (≥ 2.2.1),lgr (≥0.4.4) |
| Suggests: | caret (≥ 6.0),MASS (≥ 7.3) |
| Published: | 2023-03-17 |
| DOI: | 10.32614/CRAN.package.spFSR |
| Author: | David Akman [aut, cre], Babak Abbasi [aut, ctb], Yong Kai Wong [aut, ctb], Guo Feng Anders Yeo [aut, ctb], Zeren D. Yenice [ctb] |
| Maintainer: | David Akman <david.v.akman at gmail.com> |
| BugReports: | https://github.com/yongkai17/spFSR/issues |
| License: | GPL-3 |
| URL: | https://www.featureranking.com/ |
| NeedsCompilation: | no |
| CRAN checks: | spFSR results |