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English(EN) Evolutionary Feature Engineering for Structured Data

大语言模型驱动结构化数据的自动化特征工程

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了进化特征工程（EFE），一个利用大语言模型（LLMs）自动发现结构化数据预处理转换的新框架。EFE将这些转换表示为Python程序，能够无缝集成到现有的机器学习管道中。该框架利用数据集上下文、汇总统计和下游性能反馈来优化候选程序。EFE在时间序列预测中已取得成功，使用Chronos-2等模型将误差降低了3%或更多；在表格预测中，它能够演化出紧凑、可解释的特征程序，其性能与现有的基于LLM的方法相当或更优。 AI

影响 自动化复杂的数据预处理，有望提高各领域机器学习模型的准确性和可解释性。

排序理由 该集群描述了一篇关于使用大语言模型进行特征工程的新颖框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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大语言模型驱动结构化数据的自动化特征工程

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该集群描述了一篇关于使用大语言模型进行特征工程的新颖框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ege Onur Taga, Yilin Zhuang, M. Emrullah Ildiz, Petros Mol, Abhimanyu Das, Karthik Duraisamy, Samet Oymak ·

    Evolutionary Feature Engineering for Structured Data

    arXiv:2607.01548v1 Announce Type: cross Abstract: Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing tran…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Evolutionary Feature Engineering for Structured Data

    Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing transformations for structured data. EFE represents tr…

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