Strengthening learning algorithms by feature discovery

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Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “More recently, greedy search (Dor & Reich, 2012; Khurana et al., 2016) and depth-first search (Lam et al., 2017) in combination with feature selection have been used to create a sequence of operators.”
Feature Engineering for Predictive Modeling using Reinforcement Learning (2017, methodology): “FEADIS (Dor and Reich 2012) relies on a combination of random feature generation and feature selection.”
Learning Feature Engineering for Classification (2017, methodology): “The FEADIS algorithm of [Dor and Reich, 2012] relies on a combination of random feature generation and feature selection.”