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Feature Selection via Simultaneous Perturbation Stochastic Approximation
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This repository is a collection of methods using Simultaneous Perturbation Stochastic Approximation (SPSA) for Machine Learning. It currently contains an implementation of feature selection and ranking via SPSA based on V. Aksakalli and M. Malekipirbazari (Pattern Recognition Letters, 2016) and Zeren D. Yenice et al. (, 2018). This algorithm searches for a locally optimal set of features that yield the best predictive performance using a specified error measure such as mean squared error (for regression problems) and accuracy rate (for classification problems).

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