English(EN) SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data

新方法结合符号回归和LLM实现自动化特征工程

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员开发了SymboLLM-FE，一种将符号回归与大型语言模型（LLM）相结合，用于表格数据自动化特征工程的新方法。该方法旨在克服传统自动化特征工程的局限性，如可解释性差和需要LLM进行多次迭代，同时减轻偏见和幻觉的风险。通过符号回归提取数学表达能力强的公式，然后用LLM进行精炼以增强可解释性，SymboLLM-FE在六个真实世界数据集和四个Kaggle竞赛中展示了优于现有方法的性能，在LLM调用次数显著减少的情况下实现了高实用性。 AI

影响这种方法可能为表格数据带来更具可解释性和效率的特征工程，从而在各种应用中提高模型性能。

排序理由该集群描述了一篇详细介绍自动化特征工程新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou, Yu-Feng Li, Lan-Zhe Guo · 2026-08-31 04:00

SymboLLM-FE：LLM 加速的符号回归用于表格数据的自动化特征工程

arXiv:2608.28408v1 Announce Type: new Abstract: Tabular data, as a core data format in machine learning, often lacks the discriminative power needed for high-performance modeling due to insufficient feature informativeness. Automated Feature Engineering (AutoFE) overcomes this by…

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SymboLLM-FE：LLM 加速的符号回归用于表格数据的自动化特征工程

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