QUBO决策树：退火机扩展决策树拆分
QUBO Decision Tree: Annealing Machine Extends Decision Tree Splitting
摘要：
本文提出了一种基于二次无约束二元优化（QUBO）的回归树扩展方法。回归树是非常流行的预测模型，可以用表格数据集进行训练，但由于决策规则过于简单，因此其准确性不足。该方法将决策树中的决策规则扩展到多维边界。由于计算限制，这种扩展通常无法实现，然而，所提出的方法将训练过程转换为QUBO，这使得退火机能够解决这个问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
