Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization

--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. T o 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 operation, and enhances exploration stability. This graph also provides full traceability to empower TCTO to reuse high-utility subgraphs from historical transformations. T o demonstrate the efficacy and adaptability of our approach, we conduct comprehensive experiments and case studies, which show superior performance across a range of datasets. LASSICAL machine learning (ML) heavily relies on the structure of the model and the quality of the involving features [1]-[4]. This dependency makes designing effective features a crucial step before the learning process. Traditionally, designing effective features required extensive manual intervention, where scientists applied mathematical transformations to raw data to create meaningful ones [5], [6]. This process, illustrated in Figure 1, is known as feature transformation [7]-[9]. Xiaohan Huang, Zhiyuan Ning and Qingqing Long are with the Computer Network Information Center, Chinese Academy of Sciences, and the University of the Chinese Academy of Sciences. Yi Du, Y uanchun Zhou, and Meng Xiao are with the Computer Network Information Center, Chinese Academy of Sciences. Dongjie Wang is with the Department of Electrical Engineering and Computer Science at the University of Kansas. Ziyue Qiao is with the School of Computing and Information Technology, Great Bay University, Dongguan, China.