spFSR: Feature Selection and Ranking by Simultaneous Perturbation Stochastic Approximation

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
OS X binaries: r-release: spFSR_1.0.0.tgz, r-oldrel: spFSR_1.0.0.tgz


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