spFSR
Feature Selection and Ranking via Simultaneous Perturbation Stochastic Approximation
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).
- Version2.0.4
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release03/17/2023
Team
David Akman
Babak Abbasi
Show author detailsRolesAuthor, ContributorYong Kai Wong
Show author detailsRolesAuthor, ContributorGuo Feng Anders Yeo
Show author detailsRolesAuthor, ContributorZeren D. Yenice
Show author detailsRolesContributor
Insights
Last 30 days
This package has been downloaded 175 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 1 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,460 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 33 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends2 packages
- Imports5 packages
- Suggests2 packages