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.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Romaric Gaudel, Michèle Sebag
• Keywords: Feature Selection, Feature Uct SElection, ICML 2010, Machine Learning, Paper Formalizes Feature