QUBO Decision Tree: Annealing Machine Extends Decision Tree Splitting

This paper proposes an extension of regression trees by quadratic unconstrained binary optimization (QUBO). Regression trees are very popular prediction models that are trainable with tabular datasets, but their accuracy is insufficient because the decision rules are too simple. The proposed method...

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Bibliographic Details
Published inarXiv.org
Main Authors Yawata, Koichiro, Osakabe, Yoshihiro, Okuyama, Takuya, Asahara, Akinori
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.03.2023
Subjects
Annealing
Computer Science - Learning
Decision trees
Optimization
Physics - Data Analysis, Statistics and Probability
Prediction models
Regression analysis
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Summary:This paper proposes an extension of regression trees by quadratic unconstrained binary optimization (QUBO). Regression trees are very popular prediction models that are trainable with tabular datasets, but their accuracy is insufficient because the decision rules are too simple. The proposed method extends the decision rules in decision trees to multi-dimensional boundaries. Such an extension is generally unimplementable because of computational limitations, however, the proposed method transforms the training process to QUBO, which enables an annealing machine to solve this problem.
ISSN:2331-8422
DOI:10.48550/arxiv.2303.09772