用多智能体+记忆模块，自动生成更优的表格特征
Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data
- 多智能体分工协作，动态激活不同功能模块
- 引入记忆机制，迭代优化特征质量与多样性
- 适合需要高质量自动化特征工程的场景
自动化特征生成从原始表格数据中提取信息丰富特征，无需人工干预，对实现准确、可泛化的机器学习至关重要。传统方法依赖预定义算子库，无法利用任务语义，难以生成复杂任务下的多样化高价值特征。近期基于大语言模型（LLM）的方法引入了更丰富的语义信号，但仍受限于固定生成模式且缺乏学习目标反馈，导致特征空间狭窄。为此，我们提出一种基于记忆增强的LLM多智能体系统（MALMAS），将生成过程分解为具有不同职责的智能体，路由代理每轮激活合适子集，进一步拓展特征空间探索。我们还集成包含过程记忆、反馈记忆和概念记忆的记忆模块，支持迭代优化，自适应引导后续特征生成，提升特征质量与多样性。在多个公开数据集上的大量实验表明，该方法优于现有先进基线。代码已开源：https://github.com/fxdong24/MALMAS
原文摘要 · Abstract (English)
Automated feature generation extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning. Traditional methods rely on predefined operator libraries and cannot leverage task semantics, limiting their ability to produce diverse, high-value features for complex tasks. Recent Large Language Model (LLM)-based approaches introduce richer semantic signals, but still suffer from a restricted feature space due to fixed generation patterns and from the absence of feedback from the learning objective. To address these challenges, we propose a Memory-Augmented LLM-based Multi-Agent System (\textbf{MALMAS}) for automated feature generation. MALMAS decomposes the generation process into agents with distinct responsibilities, and a Router Agent activates an appropriate subset of agents per iteration, further broadening exploration of the feature space. We further integrate a memory module comprising procedural memory, feedback memory, and conceptual memory, enabling iterative refinement that adaptively guides subsequent feature generation and improves feature quality and diversity. Extensive experiments on multiple public datasets against state-of-the-art baselines demonstrate the effectiveness of our approach. The code is available at https://github.com/fxdong24/MALMAS
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