Séminaire Donnees et APprentissage Artificiel
Feature Selection as a One-Player Game
Intervenant(s) : Romaric Gaudel (LRI, Université Paris-Sud)
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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