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  "Title": "Feature Selection and Ranking by Simultaneous Perturbation\nStochastic Approximation",
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  "Date": "2018-05-10",
  "Authors@R": "c(\nperson(\"Vural\", \"Aksakalli\", email = \"vaksakalli@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Babak\", \"Abbasi\", email = \"babak.abbasi@rmit.edu.au\", role = c(\"aut\", \"ctb\")),\nperson(\"Yong Kai\", \"Wong\", email = \"yongkai1017@gmail.com\", role = c(\"aut\", \"ctb\")),\nperson(\"Zeren\", \"D. Yenice\", email = \"zerendyenice@gmail.com\", role = \"ctb\"))",
  "Description": "An implementation of feature selection and ranking via\nsimultaneous perturbation stochastic approximation (SPSA-FSR)\nbased on works by V. Aksakalli and M. Malekipirbazari (2015)\n<arXiv:1508.07630> and Zeren D. Yenice and et al. (2018)\n<arXiv:1804.05589>. The SPSA-FSR algorithm searches for a\nlocally optimal set of features that yield the best predictive\nperformance using a specified error measure such as mean\nsquared error (for regression problems) and accuracy rate (for\nclassification problems). This package requires an object of\nclass 'task' and an object of class 'Learner' from the 'mlr'\npackage.",
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  "URL": "https://www.featureranking.com/, https://arxiv.org/abs/1804.05589",
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  "Date/Publication": "2018-10-02 12:14:06 UTC",
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