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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).

Versions across snapshots

VersionRepositoryFileSize
2.0.4 rolling linux/jammy R-4.5 spFSR_2.0.4.tar.gz 72.4 KiB
2.0.4 rolling linux/noble R-4.5 spFSR_2.0.4.tar.gz 72.4 KiB
2.0.4 rolling source/ R- spFSR_2.0.4.tar.gz 15.8 KiB
2.0.4 latest linux/jammy R-4.5 spFSR_2.0.4.tar.gz 72.4 KiB
2.0.4 latest linux/noble R-4.5 spFSR_2.0.4.tar.gz 72.4 KiB
2.0.4 latest source/ R- spFSR_2.0.4.tar.gz 15.8 KiB
2.0.4 2026-04-26 source/ R- spFSR_2.0.4.tar.gz 15.8 KiB
2.0.4 2026-04-23 source/ R- spFSR_2.0.4.tar.gz 15.8 KiB
2.0.4 2026-04-09 windows/windows R-4.5 spFSR_2.0.4.zip 74.8 KiB
2.0.4 2025-04-20 source/ R- spFSR_2.0.4.tar.gz 15.8 KiB

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