Meta-tree random forest: Probabilistic data-generative model and bayes optimal prediction

Nao Dobashi, Shota Saito*, Yuta Nakahara, Toshiyasu Matsushima

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

This paper deals with a prediction problem of a new targeting variable corresponding to a new explanatory variable given a training dataset. To predict the targeting variable, we consider a model tree, which is used to represent a conditional probabilistic structure of a targeting variable given an explanatory variable, and discuss statistical optimality for prediction based on the Bayes decision theory. The optimal prediction based on the Bayes decision theory is given by weighting all the model trees in the model tree candidate set, where the model tree candidate set is a set of model trees in which the true model tree is assumed to be included. Because the number of all the model trees in the model tree candidate set increases exponentially according to the maximum depth of model trees, the computational complexity of weighting them increases exponentially according to the maximum depth of model trees. To solve this issue, we introduce a notion of meta-tree and propose an algorithm called MTRF (Meta-Tree Random Forest) by using multiple meta-trees. Theoretical and experimental analyses of the MTRF show the superiority of the MTRF to previous decision tree-based algorithms.

本文言語English
論文番号768
ジャーナルEntropy
23
6
DOI
出版ステータスPublished - 2021 6

ASJC Scopus subject areas

  • 情報システム
  • 数理物理学
  • 物理学および天文学(その他)
  • 電子工学および電気工学

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