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 |