English(EN) CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization

新框架融合LLM和贝叶斯优化实现AutoML

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-22 04:00

研究人员开发了CoFEH，一个将大型语言模型（LLM）与贝叶斯超参数优化（HPO）相结合的端到端自动化机器学习新框架。该系统使用具有思维树（Tree of Thought）方法的LLM来生成灵活的特征工程流水线，并使用贝叶斯优化模块进行HPO。CoFEH独特地交织了这些过程，允许在特征工程和超参数调优之间进行知情决策，与现有方法相比，其性能得到了提升。 AI

影响该框架可以通过自动化复杂的特征工程和超参数调优过程来简化机器学习模型的开发。

排序理由该集群包含一篇详细介绍自动化机器学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍自动化机器学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 103 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Beicheng Xu, Keyao Ding, Wei Liu, Yupeng Lu, Bin Cui · 2026-05-22 04:00

CoFEH：LLM驱动的特征工程，由协同贝叶斯超参数优化赋能

arXiv:2602.09851v2 Announce Type: replace Abstract: Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottleneck for traditional methods, which operate within rigid search spaces and lack domain awareness. While Large Language Models (LLMs) …

本文如何被排名

报道来源 [1]

CoFEH：LLM驱动的特征工程，由协同贝叶斯超参数优化赋能

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