Toward Provably Correct Feature Selection in Arbitrary Domains

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Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Margaritis (2009) used a combination of forward and backward selection to select a feature-subset while Gaudel and Sebag (2010) proposed to model the subset selection as a reinforcement problem.”
Survey on Automated Machine Learning (2019, methodology): “algorithm. Simple approaches use random search or test the power set exhaustively (Dash and Liu, 1997). Heuristic approaches follow an iterative procedure by adding single features (Kononenko, 1994). Margaritis (2009) used a combination of forward and backward selection to select a feature-subset wh”
Feature Selection as a One-Player Game (2010, methodology): “Hill-climbing approaches (Hall, 2000), mixing forward and backward selection (Zhang, 2008a), using look-ahead or making large steps in the lattice (Margaritis, 2009), combining global and local search operators (Boull´e, 2007), among many other heuristics, have been proposed to address the EvE…”