GFS: Graph-based Feature Synthesis for Prediction over Relational Databases
This addresses the challenge of efficiently leveraging relational data for predictions, offering a novel approach that could benefit data mining applications, though it appears incremental as it builds on graph-based methods.
The paper tackles the problem of applying machine learning to relational databases by proposing GFS, a framework that formulates databases as heterogeneous graphs to preserve relational structure, eliminating manual feature engineering and outperforming previous methods on four real-world databases.
Relational databases are extensively utilized in a variety of modern information system applications, and they always carry valuable data patterns. There are a huge number of data mining or machine learning tasks conducted on relational databases. However, it is worth noting that there are limited machine learning models specifically designed for relational databases, as most models are primarily tailored for single table settings. Consequently, the prevalent approach for training machine learning models on data stored in relational databases involves performing feature engineering to merge the data from multiple tables into a single table and subsequently applying single table models. This approach not only requires significant effort in feature engineering but also destroys the inherent relational structure present in the data. To address these challenges, we propose a novel framework called Graph-based Feature Synthesis (GFS). GFS formulates the relational database as a heterogeneous graph, thereby preserving the relational structure within the data. By leveraging the inductive bias from single table models, GFS effectively captures the intricate relationships inherent in each table. Additionally, the whole framework eliminates the need for manual feature engineering. In the extensive experiment over four real-world multi-table relational databases, GFS outperforms previous methods designed for relational databases, demonstrating its superior performance.