标题： 基于强化学习的科学数据特征生成算法 显示英文标题

标题： Reinforcement Learning-based Feature Generation Algorithm for Scientific Data

摘要： 特征生成（FG）旨在通过构建高阶特征组合并去除冗余特征来增强原始数据的预测潜力。它是表格型科学数据的关键预处理步骤，以提高下游机器学习模型的性能。传统方法在处理科学数据的特征生成时面临以下两个挑战：首先，科学数据中有效构建高阶特征组合需要深刻且广泛的专业领域知识。其次，随着特征组合的阶数增加，搜索空间呈指数级扩展，导致人力消耗巨大。数据驱动的人工智能（DCAI）范式的进展为自动化特征生成过程开辟了新途径。受此启发，本文重新审视传统的特征生成工作流程，并提出了多智能体特征生成（MAFG）框架。具体而言，在迭代探索阶段，多智能体会协作构建数学变换方程，合成并识别表现出高信息量的特征组合，并利用强化学习机制演化其策略。在完成探索阶段后，MAFG整合大语言模型（LLMs）对每个重要模型性能突破生成的特征进行解释性评估。实验结果和案例研究一致表明，MAFG框架能够有效自动化特征生成过程，并显著提升各种下游科学数据挖掘任务。 显示英文摘要

摘要： Feature generation (FG) aims to enhance the prediction potential of original data by constructing high-order feature combinations and removing redundant features. It is a key preprocessing step for tabular scientific data to improve downstream machine-learning model performance. Traditional methods face the following two challenges when dealing with the feature generation of scientific data: First, the effective construction of high-order feature combinations in scientific data necessitates profound and extensive domain-specific expertise. Secondly, as the order of feature combinations increases, the search space expands exponentially, imposing prohibitive human labor consumption. Advancements in the Data-Centric Artificial Intelligence (DCAI) paradigm have opened novel avenues for automating feature generation processes. Inspired by that, this paper revisits the conventional feature generation workflow and proposes the Multi-agent Feature Generation (MAFG) framework. Specifically, in the iterative exploration stage, multi-agents will construct mathematical transformation equations collaboratively, synthesize and identify feature combinations ex-hibiting high information content, and leverage a reinforcement learning mechanism to evolve their strategies. Upon completing the exploration phase, MAFG integrates the large language models (LLMs) to interpreta-tively evaluate the generated features of each significant model performance breakthrough. Experimental results and case studies consistently demonstrate that the MAFG framework effectively automates the feature generation process and significantly enhances various downstream scientific data mining tasks.