Feature Selection as a One-Player Game

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Feature Selection as a One-Player Game
This paper formalizes Feature Selection as a Reinforcement Learning problem, leading to a provably optimal though intractable selection policy. As a second contribution, this paper presents an approximation thereof, based on a one-player game approach and relying on the Monte-Carlo tree search UCT (Upper Confidence Tree) proposed by Kocsis and Szepesvari (2006). More precisely, the presented FUSE (Feature Uct SElection) algorithm extends UCT to deal with i) a finite unknown horizon (the target number of relevant features); ii) a huge branching factor of the search tree (the size of the initial feature set). Additionally, a frugal reward function is proposed as a rough but unbiased estimate of the relevance of a feature subset. A proof of concept of FUSE is shown on the NIPS 2003 Feature Selection Challenge.
Romaric Gaudel, Michèle Sebag
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Romaric Gaudel, Michèle Sebag
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