用多智能体强化学习自动优化特征转换路径，提升下游模型效果。
Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization
- 构建动态交互图，将特征和转换操作建模为节点与边。
- 通过剪枝与回溯减少冗余，提升探索稳定性并保留历史高效子图。
- 适合自动化特征工程需求的工业场景与数据科学家使用。
特征转换旨在寻找最优的数学特征交叉过程，生成高价值特征以提升下游机器学习任务性能。现有框架虽降低了人工成本，但常将特征转换视为孤立操作，忽视步骤间的动态依赖。为此，我们提出TCTO——一种基于图驱动路径优化的协同多智能体强化学习框架，实现自动化特征工程。其核心创新在于一个动态演进的交互图，将特征作为节点、转换操作作为边。通过图剪枝与回溯机制，动态剔除低效边，减少冗余操作，并增强探索稳定性；同时支持全链路可追溯性，使TCTO能复用历史中高价值子图。我们在多个数据集上开展全面实验与案例研究，验证了该方法在性能与适应性上的优越性。
原文摘要 · Abstract (English)
Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream machine learning tasks. Existing frameworks, though designed to mitigate manual costs, often treat feature transformations as isolated operations, ignoring dynamic dependencies between transformation steps. To address the limitations, we propose TCTO, a collaborative multi-agent reinforcement learning framework that automates feature engineering through graph-driven path optimization. The framework's core innovation lies in an evolving interaction graph that models features as nodes and transformations as edges. Through graph pruning and backtracking, it dynamically eliminates low-impact edges, reduces redundant operations, and enhances exploration stability. This graph also provides full traceability to empower TCTO to reuse high-utility subgraphs from historical transformations. To demonstrate the efficacy and adaptability of our approach, we conduct comprehensive experiments and case studies, which show superior performance across a range of datasets.
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