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
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- Depends2 packages
- Imports5 packages
- Suggests2 packages