spFSR: Feature Selection and Ranking by Simultaneous Perturbation Stochastic Approximation

An implementation of feature selection and ranking via simultaneous perturbation stochastic approximation (SPSA-FSR) based on works by V. Aksakalli and M. Malekipirbazari (2015) <arXiv:1508.07630> and Zeren D. Yenice and et al. (2018) <arXiv:1804.05589>. The SPSA-FSR 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). This package requires an object of class 'task' and an object of class 'Learner' from the 'mlr' package.

Package details

AuthorVural Aksakalli [aut, cre], Babak Abbasi [aut, ctb], Yong Kai Wong [aut, ctb], Zeren D. Yenice [ctb]
MaintainerVural Aksakalli <[email protected]>
URL https://www.featureranking.com/ https://arxiv.org/abs/1804.05589
Package repositoryView on CRAN
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spFSR documentation built on May 11, 2018, 5:05 p.m.