Algebraic Model Counting for Global Analysis of Optimal Decision Trees

A theory and application paper proposing a novel framework for exhaustive analysis of optimal and near-optimal decision trees, called Algebraic Decision Tree Counting (ADTC), inspired by the Algebraic Model Counting (AMC) in knowledge representation and numeric-symbol reasoning. An implementation of this tensor manipulation framework, the emtrees software, is publicly available in an open repository (https://doi.org/10.5281/zenodo.20842908).

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-25
DOI
https://doi.org/10.5281/zenodo.20846128
Primary Topic
Bayesian Modeling and Causal Inference
Type
preprint
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preprint

Algebraic Model Counting for Global Analysis of Optimal Decision Trees

Hiroki Arimura
Zenodo (CERN European Organization for Nuclear Research)
Bayesian Modeling and Causal Inference
preprint

Algebraic Model Counting for Global Analysis of Optimal Decision Trees

Hiroki Arimura
preprint en

Abstract

A theory and application paper proposing a novel framework for exhaustive analysis of optimal and near-optimal decision trees, called Algebraic Decision Tree Counting (ADTC), inspired by the Algebraic Model Counting (AMC) in knowledge representation and numeric-symbol reasoning. An implementation of this tensor manipulation framework, the emtrees software, is publicly available in an open repository (https://doi.org/10.5281/zenodo.20842908).

Zenodo (CERN European Organization for Nuclear Research)
Hokkaido University (JP)
Peace, Justice and strong institutions
Bayesian Modeling and Causal Inference
DOIOpen Access PDFOpenAlex
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Algebraic Model Counting for Global Analysis of Optimal Decision Trees — Hiroki Arimura · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS