用大模型自动生成多标签任务特征，还能自我优化。
The Semantic Architect: How FEAML Bridges Structured Data and LLMs for Multi-Label Tasks
- 用元数据和标签共现矩阵引导大模型生成特征
- 自动生成特征后通过准确率与相关性评估效果
- 支持迭代优化，适合需要可解释特征的场景
现有基于大语言模型（LLMs）的特征工程方法尚未应用于多标签学习任务，缺乏对复杂标签依赖关系的建模能力，且未针对多标签特性进行适配。为此，我们提出面向多标签学习的特征工程自动化方法（FEAML），利用大模型的代码生成能力，结合元数据和标签共现矩阵，引导其理解特征与任务目标间的关系，从而生成高质量特征。新生成的特征通过模型准确率评估有效性，并用皮尔逊相关系数检测冗余。将评估结果作为反馈，驱动大模型在后续迭代中持续优化代码生成。通过融合大模型与反馈机制，FEAML实现了高效、可解释且自提升的特征工程范式。在多个多标签数据集上的实验表明，FEAML优于其他特征工程方法。
原文摘要 · Abstract (English)
Existing feature engineering methods based on large language models (LLMs) have not yet been applied to multi-label learning tasks. They lack the ability to model complex label dependencies and are not specifically adapted to the characteristics of multi-label tasks. To address the above issues, we propose Feature Engineering Automation for Multi-Label Learning (FEAML), an automated feature engineering method for multi-label classification which leverages the code generation capabilities of LLMs. By utilizing metadata and label co-occurrence matrices, LLMs are guided to understand the relationships between data features and task objectives, based on which high-quality features are generated. The newly generated features are evaluated in terms of model accuracy to assess their effectiveness, while Pearson correlation coefficients are used to detect redundancy. FEAML further incorporates the evaluation results as feedback to drive LLMs to continuously optimize code generation in subsequent iterations. By integrating LLMs with a feedback mechanism, FEAML realizes an efficient, interpretable and self-improving feature engineering paradigm. Empirical results on various multi-label datasets demonstrate that our FEAML outperforms other feature engineering methods.
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