|Title||Reinforcement Learning Trees.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Zhu, Ruoqing, Donglin Zeng, and Michael R. Kosorok|
|Journal||J Am Stat Assoc|
In this paper, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman, 2001) under high-dimensional settings. The innovations are three-fold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future improvement in later splits, rather than choosing the one with largest marginal effect from the immediate split, the constructed tree utilizes the available samples in a more efficient way. Moreover, such an approach enables linear combination cuts at little extra computational cost. Second, we propose a variable muting procedure that progressively eliminates noise variables during the construction of each individual tree. The muting procedure also takes advantage of reinforcement learning and prevents noise variables from being considered in the search for splitting rules, so that towards terminal nodes, where the sample size is small, the splitting rules are still constructed from only strong variables. Last, we investigate asymptotic properties of the proposed method under basic assumptions and discuss rationale in general settings.
|Alternate Journal||J Am Stat Assoc|
|Original Publication||Reinforcement learning trees.|
|PubMed Central ID||PMC4760114|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA082659 / CA / NCI NIH HHS / United States
U01 NS082062 / NS / NINDS NIH HHS / United States
Reinforcement Learning Trees.