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User sebag
title Feature Selection as a one-player game.
  • Gaudel, R. and Sebag, M.
Source "Proceedings of the 27th Annual International Conference on Machine Learning (ICML 2010)" : p. 359–366. 2010.
Year 2010
Type Conference
Type of publication ONCE-CS

Optional Infos

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.

author = {Gaudel, R. and Sebag, M.},
title = {Feature {S}election as a one-player game},
year = {2010},
x-theme = {decision},
x-equipe = {IAO},
x-support = {actes},
x-type = Poste CR2 'Cognition Sociale' fléché vers le CREA en 2010.
Mots clé: modélisation de systèmes sociaux, reconstruction de dynamiques sociales, systèmes complexes, sciences cognitives

Created Tuesday 10 May, 2011 17:17:02