Feature Selection as a One-Player GameReportar como inadecuado

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1 LRI - Laboratoire de Recherche en Informatique 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.

Keywords : feature selection combinatorial optimization Upper Bound confidence Tree

Autor: Romaric Gaudel - Michèle Sebag -

Fuente: https://hal.archives-ouvertes.fr/


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