spFSR: Feature Selection and Ranking via Simultaneous Perturbation Stochastic Approximation
David Akman, Babak Abbasi, Yong Kai Wong, Guo Feng Anders Yeo · 2018
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).