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IPM
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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 13359
School of Cognitive Sciences
  Title:   Bandit-based local feature subset selection
  Author(s): 
1.  M.H. Zokaei Ashtiani
2.  M. Nili Ahmad Abadi
3.  B. Nadjar Araabi
  Status:   Published
  Journal: Neurocomputing
  Vol.:  138
  Year:  2014
  Pages:   371-382
  Supported by:  IPM
  Abstract:
In this work we propose a method for local feature subset selection, where we simultaneously partition the sample space into localities and select features for them. The partitions and the corresponding local features are represented using a novel notion of feature tree. The problem of finding an appropriate feature tree is then formulated as a reinforcement learning problem. A value-based Monte Carlo tree search with the corresponding credit assignment policy is devised to learn near-optimal feature trees. Furthermore, the Monte Carlo tree search is enhanced in a way to be applicable for large numbers of actions (i.e., features).

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