Feature Selection as a One-Player Game

Romaric Gaudel 1 Michèle Sebag 1, 2
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : 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). The Feature Uct SElection (FUSE) algorithm extends UCT to deal with i) a finite unknown horizon (the target number of relevant features); ii) the huge branching factor of the search tree, reflecting the size of the feature set. Finally, 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 benchmark data sets.
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Submitted on : Monday, May 17, 2010 - 5:03:21 PM
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  • HAL Id : inria-00484049, version 1



Romaric Gaudel, Michèle Sebag. Feature Selection as a One-Player Game. International Conference on Machine Learning, Jun 2010, Haifa, Israel. pp.359--366. ⟨inria-00484049⟩



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