用语义引导强化学习，自动生成可解释的特征
Semantic-Guided RL for Interpretable Feature Engineering
- 结合知识图谱语义与深度强化学习，分两步生成特征
- 在多个公开数据集上提升预测准确率，且特征可解释性高
- 适合需要可解释特征的领域，如医疗、金融建模
机器学习模型的性能高度依赖输入数据质量，因此生成高质量特征至关重要，这一过程称为特征工程（FE）。由于人工特征工程耗时且需领域知识，自动化特征工程（AutoFE）尤为关键。当前主要挑战在于生成可解释的特征。为此，本文提出SMART，一种混合方法：利用描述逻辑（DL）在知识图谱（KG）中推理出领域特定特征（利用阶段），再通过深度强化学习（DRL）对搜索空间进行有指导的探索（探索阶段）。在多个公开数据集上的实验表明，SMART显著提升了预测准确率，并保证了高可解释性。
原文摘要 · Abstract (English)
The quality of Machine Learning (ML) models strongly depends on the input data, as such generating high-quality features is often required to improve the predictive accuracy. This process is referred to as Feature Engineering (FE). However, since manual feature engineering is time-consuming and requires case-by-case domain knowledge, Automated Feature Engineering (AutoFE) is crucial. A major challenge that remains is to generate interpretable features. To tackle this problem, we introduce SMART, a hybrid approach that uses semantic technologies to guide the generation of interpretable features through a two-step process: Exploitation and Exploration. The former uses Description Logics (DL) to reason on the semantics embedded in Knowledge Graphs (KG) to infer domain-specific features, while the latter exploits the knowledge graph to conduct a guided exploration of the search space through Deep Reinforcement Learning (DRL). Our experiments on public datasets demonstrate that SMART significantly improves prediction accuracy while ensuring a high level of interpretability.
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