发表机构
Virginia Tech（弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究表格学习中自动特征工程问题，提出拓扑感知多岛进化框架TOPOFE，结合特定族探索、自适应提示记忆和拓扑引导知识转移，在多个公共数据集实验中，相比现有方法有改进，能发现更多样可转移特征程序。
AI 中文摘要
表格学习中的自动特征工程（AutoFE）可自然地表述为程序合成问题，目标是从指数级大的搜索空间中发现预测性特征变换。大语言模型（LLMs）的进展通过实现超越预定义算子库的特征程序生成扩展了AutoFE的表现力。然而，现有基于LLM的方法在无状态生成和同质搜索方面存在根本限制。我们提出TOPOFE，一种用于LLM引导特征工程的拓扑感知多岛进化框架。它结合了特定族探索、自适应提示记忆和拓扑引导的知识转移，以有效发现多样且可组合的特征程序。在29个公共表格数据集上的实验表明，在分类和回归任务中，它比现有方法有持续改进，还能发现更多样且可转移的特征程序。
英文摘要
Automatic feature engineering (AutoFE) for tabular data requires discovering informative transformations from a large program space. Existing approaches suffer from three limitations: classical methods rely on fixed operator libraries with limited expressivity, LLM-based methods generate proposals from static prompts without retaining search experience, and evolutionary methods use fixed migration policies that ignore task-specific cross-family transfer utility. We introduce TOPOFE, a framework that formulates AutoFE as graph-structured multi-island evolutionary program search. The transformation space is partitioned into semantically coherent families, each explored by an island through LLM-guided mutation and crossover. Each island maintains a Prompt Adaptation Memory that accumulates accept/reject feedback to steer proposals toward productive regions without parameter updates. To coordinate global exploration, TOPOFE dynamically learns a directed topology graph whose edge weights encode transfer utility between transformation families. Cross-island transfer is triggered by adaptive saturation detection and performed through LLM-mediated hybrid synthesis, enabling discovery of compositional feature programs that cannot emerge from isolated local search. Experiments on 29 tabular datasets show that TOPOFE consistently outperforms most state-of-the-art AutoFE methods on classification and regression tasks. Beyond predictive performance, TOPOFE produces feature sets with lower redundancy and higher representational coverage, while the learned topology graph acquires meaningful task-specific transfer structure correlated with downstream gains. The discovered feature programs transfer reliably across diverse predictors and LLM backbones, demonstrating that improvements arise from TOPOFE's structured search and adaptive coordination rather than backbone-specific generation capability.
