基于交互信息的自动特征工程，提升预测性能。
IIFE: Interaction Information Based Automated Feature Engineering
- 用信息论中的交互信息识别特征组合的协同效应。
- 在多个数据集上优于现有AutoFE算法。
- 适用于缺乏领域知识的数据科学新手。
自动特征工程（AutoFE）旨在自动构建和选择能提升下游预测性能的新特征。传统特征工程依赖大量领域知识和反复试验，而AutoFE致力于让所有数据科学从业者都能轻松使用。本文提出一种基于交互信息的新型AutoFE算法IIFE，通过信息论视角判断特征对之间的协同效果。实验表明，IIFE在多个基准数据集上显著优于现有算法。此外，我们还展示了如何利用交互信息改进已有AutoFE方法。最后，我们指出了现有AutoFE研究中若干关键实验设置问题及其对性能评估的影响。
原文摘要 · Abstract (English)
Automated feature engineering (AutoFE) is the process of automatically building and selecting new features that help improve downstream predictive performance. While traditional feature engineering requires significant domain expertise and time-consuming iterative testing, AutoFE strives to make feature engineering easy and accessible to all data science practitioners. We introduce a new AutoFE algorithm, IIFE, based on determining which feature pairs synergize well through an information-theoretic perspective called interaction information. We demonstrate the superior performance of IIFE over existing algorithms. We also show how interaction information can be used to improve existing AutoFE algorithms. Finally, we highlight several critical experimental setup issues in the existing AutoFE literature and their effects on performance.
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