GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification
This work addresses the problem of explainable classification for users needing interpretable AI models, though it appears incremental as it combines existing techniques.
The paper tackles the challenge of achieving both accuracy and interpretability in classification by introducing GPT-HTree, a framework that integrates hierarchical clustering, decision trees, and large language models, resulting in a method that ensures tailored classification paths and human-readable insights.
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.