An implementation of feature selection and ranking via simultaneous perturbation stochastic approximation (SPSA-FSR) based on works by V. Aksakalli and M. Malekipirbazari (2015) <arXiv:1508.07630> and Zeren D. Yenice and et al. (2018) <arXiv:1804.05589>. The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using a specified error measure such as mean squared error (for regression problems) and accuracy rate (for classification problems). This package requires an object of class 'task' and an object of class 'Learner' from the 'mlr' package.
| Version: | 1.0.0 |
| Depends: | mlr (≥ 2.11), parallelMap (≥ 1.3), parallel (≥ 3.4.2), tictoc (≥ 1.0) |
| Imports: | ggplot2 (≥ 2.2.1), class (≥ 7.3), mlbench (≥ 2.1) |
| Suggests: | caret (≥ 6.0), MASS (≥ 7.3), knitr, rmarkdown |
| Published: | 2018-05-11 |
| Author: | Vural Aksakalli [aut, cre], Babak Abbasi [aut, ctb], Yong Kai Wong [aut, ctb], Zeren D. Yenice [ctb] |
| Maintainer: | Vural Aksakalli <vaksakalli at gmail.com> |
| BugReports: | https://github.com/yongkai17/spFSR/issues |
| License: | GPL-3 |
| URL: | https://www.featureranking.com/, https://arxiv.org/abs/1804.05589 |
| NeedsCompilation: | no |
| CRAN checks: | spFSR results |
| Reference manual: | spFSR.pdf |
| Vignettes: |
Introduction to 'spFSR' - feature selection and ranking by simultaneous perturbation stochastic approximation |
| Package source: | spFSR_1.0.0.tar.gz |
| Windows binaries: | r-devel: spFSR_1.0.0.zip, r-release: spFSR_1.0.0.zip, r-oldrel: spFSR_1.0.0.zip |
| macOS binaries: | r-release: spFSR_1.0.0.tgz, r-oldrel: spFSR_1.0.0.tgz |
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