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June 12, 2026Annals of Mathematics and Artificial Intelligence0 citationsOpen Access

Reducing the number of trees in a bag of binary decision trees governed by the majority vote

TATatsuya AkutsuAMAvraham A. MelkmanATAtsuhiro Takasu

Key Points

  • This research aims to streamline the prediction phase of random forests by reducing the number of decision trees while maintaining performance.
  • Examined representation of a bag of decision trees focused on binary decision problems.
  • Demonstrated the majority function of n variables can use fewer decision trees.
  • Analyzed the representation of k-out-of-n functions in relation to decision trees.
  • Majority function of n variables can be represented by n-2c decision trees, with each tree maintaining polynomial size.
  • Allowed a small classification error, enabling the representation of n decision trees with n-2c polynomial-size trees.
  • Presented related results on k-out-of-n functions, showcasing efficiency improvements.

Abstract

In this paper, we focus on the prediction phase of a random forest and study the problem of representing a bag of decision trees using a smaller bag of decision trees, where we only consider binary decision problems on the binary domain and simple decision trees in which an internal node is limited to querying the Boolean value of a single variable. As a main result, we show that given a constant integer c the majority function of an odd number n of variables can be represented by a bag of n-2c decision trees each of which has size polynomial in n. We also show that a general bag of n decision trees can be represented by another bag containing only n-2c polynomial-size decision trees, provided a small classification error is allowed. A related result on the k-out-of-n functions is presented as well.

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Cite This Study

Akutsu et al. (2026) studied this question.

synapsesocial.com/papers/6a2ba2c18101cf8926f019f8https://doi.org/10.1007/s10472-026-10013-5
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