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Government data is essential for social and economic development, underpinning policy-making, public services, and data-driven innovation. However, the heterogeneity of government datasets significantly increases the risk of misclassification. Currently, obtaining a robust and accurate classification model for government data is challenging due to label limitation and structural complexity, resulting in poor classification performance. To address this challenge, this paper utilizes a tree-of-thoughts-driven few-shot learning approach to propose an accurate classification for government data without requiring extensive feature engineering. In our approach, we employ a tree-of-thoughts-based structure to dynamically adjust the classification process. To enhance classification accuracy, we develop a label expansion method that further extends and clarifies the original classification terms. Additionally, we design a dynamic tree generator based on semantic clustering and association to achieve accurate classification. Experiments demonstrate that our approach achieves a precision of 85.15% in government data classification, surpassing existing methods in comparative studies.

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Accurate Classification for Government Data: A Tree-of-Thoughts-Driven Few-Shot Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Mengxiang Zhu,
  • Yunchuan Guo,
  • Duo Zhang,
  • Ziyan Zhou,
  • Haoyang Yu,
  • Lingcui Zhang

摘要

Government data is essential for social and economic development, underpinning policy-making, public services, and data-driven innovation. However, the heterogeneity of government datasets significantly increases the risk of misclassification. Currently, obtaining a robust and accurate classification model for government data is challenging due to label limitation and structural complexity, resulting in poor classification performance. To address this challenge, this paper utilizes a tree-of-thoughts-driven few-shot learning approach to propose an accurate classification for government data without requiring extensive feature engineering. In our approach, we employ a tree-of-thoughts-based structure to dynamically adjust the classification process. To enhance classification accuracy, we develop a label expansion method that further extends and clarifies the original classification terms. Additionally, we design a dynamic tree generator based on semantic clustering and association to achieve accurate classification. Experiments demonstrate that our approach achieves a precision of 85.15% in government data classification, surpassing existing methods in comparative studies.