Graph-Based Feature Augmentation for Predictive Tasks on Relational Datasets
This work addresses the problem of reducing manual effort in feature engineering for data scientists and practitioners in domains like finance and healthcare, though it is incremental as it builds on existing AutoML and graph-based methods.
The paper tackled automating feature augmentation for predictive tasks on relational datasets by proposing ReCoGNN, an end-to-end framework that uses graph neural networks to extract and integrate relational signals, resulting in consistent outperformance of existing methods on ten datasets for classification and regression tasks.
Data has become a foundational asset driving innovation across domains such as finance, healthcare, and e-commerce. In these areas, predictive modeling over relational tables is commonly employed, with increasing emphasis on reducing manual effort through automated machine learning (AutoML) techniques. This raises an interesting question: can feature augmentation itself be automated and identify and utilize task-related relational signals? To address this challenge, we propose an end-to-end automated feature augmentation framework, ReCoGNN, which enhances initial datasets using features extracted from multiple relational tables to support predictive tasks. ReCoGNN first captures semantic dependencies within each table by modeling intra-table attribute relationships, enabling it to partition tables into structured, semantically coherent segments. It then constructs a heterogeneous weighted graph that represents inter-row relationships across all segments. Finally, ReCoGNN leverages message-passing graph neural networks to propagate information through the graph, guiding feature selection and augmenting the original dataset. Extensive experiments conducted on ten real-life and synthetic datasets demonstrate that ReCoGNN consistently outperforms existing methods on both classification and regression tasks.