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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).
Getting started
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Package details |
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| Author | David Akman [aut, cre], Babak Abbasi [aut, ctb], Yong Kai Wong [aut, ctb], Guo Feng Anders Yeo [aut, ctb], Zeren D. Yenice [ctb] |
| Maintainer | David Akman <david.v.akman@gmail.com> |
| License | GPL-3 |
| Version | 2.0.4 |
| URL | https://www.featureranking.com/ |
| Package repository | View on CRAN |
| Installation |
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