基于决策树的基元转化算法研究

颜伊庆; 李嘉豪; 黄思程; 李兴森

doi:10.12052/gdutxb.250229

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基于决策树的基元转化算法研究

可拓学与创新方法 | 更新时间：2026-08-04

- 基于决策树的基元转化算法研究
- A Basic-element Transformation Algorithm Based on Decision Trees
- 广东工业大学学报 2026年43卷第4期页码：122-130
- 作者机构：
  
  广东工业大学可拓学与创新方法研究所,广东,广州,510006
- 作者简介：
  
  颜伊庆(1990–)，女，副教授，博士，主要研究方向为数据库、计算生物学、可拓智能，E-mail：yanlolo2012@gmail.com
  李兴森(1968–)，男，教授，博士，主要研究方向为可拓学与智能创新工程等，E-mail：lixs@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(72071049);广东省自然科学基金资助项目(2024A1515011324);广东省珠江人才计划(2024QN11X2333);广东工业大学青年百人项目(263114040)
- DOI：10.12052/gdutxb.250229
  中图分类号： TP181
- 纸质出版：2026
- 稿件说明：
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颜伊庆, 李嘉豪, 黄思程, 等. 基于决策树的基元转化算法研究[J]. 广东工业大学学报, 2026, 43(4): 122-130.
颜伊庆, 李嘉豪, 黄思程, 等. 基于决策树的基元转化算法研究[J]. 广东工业大学学报, 2026, 43(4): 122-130. DOI： 10.12052/gdutxb.250229.

DOI： 10.12052/gdutxb.250229

摘要

当前主流深度学习模型因可解释性不足，难以满足医疗、司法、金融等风险敏感领域对决策透明度与可控性的要求。针对现有监督学习方法侧重分类预测而忽略样本特征可转化性的问题，本文引入可拓学理论，提出一种基于决策树的基元转化算法。该算法首先明确分类问题与转化问题的本质差异，建立转化问题的形式化数学模型；其次定义转化距离以量化不同特征值之间的转化难度；在此基础上，设计可嵌入任意决策树模型的通用转化机制，实现特征级转化路径的定量推导。实验结果表明，该算法在个性化健康管理场景中能够有效挖掘正可拓域样本，提升整体达标率，验证了方法的有效性与可推广性。

Abstract

Current mainstream deep learning models

due to insufficient interpretability

struggle to meet the demands for decision transparency and controllability in risk-sensitive domains such as healthcare

justice

and finance. To address the limitation that existing supervised learning methods focus on classification prediction while neglecting the transformability of sample features

this research introduces Extenics theory and proposes a basic-element transformation algorithm based on decision trees. The algorithm first clarifies the essential differences between classification and transformation problems and establishes a formal mathematical model for the transformation problem. It then defines a transformation distance to quantify the difficulty of transforming between different feature values. On this basis

a general transformation mechanism is designed which can be embedded into arbitrary decision-tree models

enabling quantitative derivation of feature-level transformation paths. Experimental results demonstrate that the proposed algorithm can effectively mine samples in the positive extension domain and improve the overall compliance rate in personalized healthcare scenarios

verifying the effectiveness and generalizability of the method.

关键词

Keywords

references

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