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| Paper IPM / Cognitive / 13359 |
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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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