Package: spFSR 1.0.0

Vural Aksakalli

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.

Authors:Vural Aksakalli [aut, cre], Babak Abbasi [aut, ctb], Yong Kai Wong [aut, ctb], Zeren D. Yenice [ctb]

spFSR_1.0.0.tar.gz
spFSR_1.0.0.zip(r-4.5)spFSR_1.0.0.zip(r-4.4)spFSR_1.0.0.zip(r-4.3)
spFSR_1.0.0.tgz(r-4.5-any)spFSR_1.0.0.tgz(r-4.4-any)spFSR_1.0.0.tgz(r-4.3-any)
spFSR_1.0.0.tar.gz(r-4.5-noble)spFSR_1.0.0.tar.gz(r-4.4-noble)
spFSR_1.0.0.tgz(r-4.4-emscripten)spFSR_1.0.0.tgz(r-4.3-emscripten)
spFSR.pdf |spFSR.html
spFSR/json (API)

# Install 'spFSR' in R:
install.packages('spFSR', repos = c('https://yongkai17.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yongkai17/spfsr/issues

On CRAN:

Conda:

4.00 score 2 stars 8 scripts 225 downloads 4 exports 42 dependencies

Last updated 7 years agofrom:0ca92c1d71. Checks:1 OK, 8 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 10 2025
R-4.5-winWARNINGMar 10 2025
R-4.5-macWARNINGMar 10 2025
R-4.5-linuxWARNINGMar 10 2025
R-4.4-winWARNINGMar 10 2025
R-4.4-macWARNINGMar 10 2025
R-4.4-linuxWARNINGMar 10 2025
R-4.3-winWARNINGMar 10 2025
R-4.3-macWARNINGMar 10 2025

Exports:getBestModelgetImportanceplotImportancespFeatureSelection

Dependencies:backportsBBmisccheckmateclassclicolorspacedata.tablefansifarverfastmatchggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmlbenchmlrmunsellnlmeparallelMapParamHelperspillarpkgconfigR6RColorBrewerrlangscalesstringisurvivaltibbletictocutf8vctrsviridisLitewithrXML

Introduction to spFSR - feature selection and ranking by simultaneous perturbation stochastic approximation

Rendered fromspFSR.Rmdusingknitr::rmarkdownon Mar 10 2025.

Last update: 2018-10-02
Started: 2018-10-02