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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 |
| 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 |
| Reference manual: | spFSR.pdf |
| Package source: | spFSR_2.0.4.tar.gz |
| Windows binaries: | r-devel: spFSR_2.0.4.zip, r-release: spFSR_2.0.4.zip, r-oldrel: spFSR_2.0.4.zip |
| macOS binaries: | r-release (arm64): spFSR_2.0.4.tgz, r-oldrel (arm64): spFSR_2.0.4.tgz, r-release (x86_64): spFSR_2.0.4.tgz, r-oldrel (x86_64): spFSR_2.0.4.tgz |
| Old sources: | spFSR archive |
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These binaries (installable software) and packages are in development.
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