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

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Authors: Tatsuya Akutsu, Avraham A. Melkman, Atsuhiro Takasu
Cited by: 0
References: 15

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.

Fields of Study & Subjects

Data Mining Algorithms and Applications Rough Sets and Fuzzy Logic Machine Learning and Data Classification

Publication details

Journal / source
Annals of Mathematics and Artificial Intelligence
Publisher
Springer Science and Business Media LLC
Publication date
10 June 2026
Language
EN