分层与置换不变的特征变换学习：基于策略引导的嵌入搜索
Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search
另 3 家 · 查看机构详情
- University of Kansas（堪萨斯大学）
- Southwest University of Finance and Economics（西南财经大学）
- University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
- University of Auckland（奥克兰大学）
- Arizona State University（亚利桑那州立大学）
- Northeast Normal University（东北师范大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对表格数据特征变换中的层次关系缺失、置换不变性偏差及梯度搜索不适配问题，提出置换不变分层模块与策略引导多目标强化学习框架，提升预测性能与效率。
中文摘要 AI 辅助
特征变换通过从原始特征构造信息丰富的抽象表示，提升了表格数据的预测性能。近期生成式方法将变换知识编码到连续嵌入空间中，以便高效探索候选策略，但面临三个关键局限：（1）忽略了低层特征、操作和高层抽象之间的层次关系；（2）在本质上置换不变的变换序列上强制使用顺序敏感的嵌入，从而引入系统性偏差；（3）依赖基于梯度的搜索，不适用于非凸的变换空间。我们提出了一个包含两个互补组件的框架。首先，一个置换不变的分层模块捕获特征、操作和抽象层级之间的交互，通过自注意力池化机制将语义等价的结构映射到与下游性能一致的嵌入。其次，一种策略引导的多目标强化学习策略从经验上较强的种子初始化搜索，并联合优化预测准确性和变换效率。在多种表格基准上的大量实验证明了我们框架相对于强基线的有效性和鲁棒性。我们的代码和数据公开于：此 https URL。
英文摘要
Feature transformation improves predictive performance on tabular data by constructing informative abstractions from raw features. Recent generative approaches encode transformation knowledge into continuous embedding spaces for efficient exploration of candidate strategies, but face three key limitations: (1) overlooking hierarchical relationships between low-level features, operations, and high-level abstractions; (2) enforcing order-sensitive embeddings on inherently permutation-invariant transformation sequences, thereby introducing systematic bias; and (3) relying on gradient-based search, which is ill-suited to non-convex transformation spaces. We propose a framework with two complementary components. First, a permutation-invariant hierarchical module captures interactions across features, operations, and abstraction levels, with a self-attention pooling mechanism that maps semantically equivalent structures to consistent embeddings aligned with downstream performance. Second, a policy-guided multi-objective reinforcement learning strategy initializes the search from empirically strong seeds and jointly optimizes predictive accuracy and transformation efficiency. Extensive experiments on diverse tabular benchmarks demonstrate the effectiveness and robustness of our framework against strong baselines. Our code and data are publicly available at: https://github.com/RayLiu1103/PHER.
