用大模型+聚类+决策树实现可解释分类，兼顾准确与可读。
GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification
- 分层聚类+重采样，按特征分组优化分类路径。
- 每个簇内用决策树建模，提升分类精度与可解释性。
- 大模型生成自然语言描述，让结果更易懂，适合业务场景。
本文提出GPT-HTree框架，融合分层聚类、决策树与大语言模型（LLMs），解决分类任务中准确率与可解释性难以兼得的问题。通过分层聚类基于显著特征对个体进行划分，结合重采样技术平衡类别分布，并在每个簇内使用决策树构建个性化分类路径，确保分类性能与可解释性。同时，利用大语言模型生成人类可读的簇描述，将量化分析结果转化为可操作的洞察，增强决策支持能力。
原文摘要 · Abstract (English)
This paper introduces GPT-HTree, a framework combining hierarchical clustering, decision trees, and large language models (LLMs) to address this challenge. By leveraging hierarchical clustering to segment individuals based on salient features, resampling techniques to balance class distributions, and decision trees to tailor classification paths within each cluster, GPT-HTree ensures both accuracy and interpretability. LLMs enhance the framework by generating human-readable cluster descriptions, bridging quantitative analysis with actionable insights.
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