# Graph-Based Feature Augmentation for Predictive Tasks on Relational Datasets

## TL;DR

This paper introduces ReCoGNN, an automated framework that enhances relational datasets with features extracted via graph neural networks, improving predictive performance on classification and regression tasks.

## Contribution

ReCoGNN is a novel end-to-end automated feature augmentation method that models intra-table dependencies and inter-row relationships using graph neural networks.

## Key findings

- ReCoGNN outperforms existing methods on multiple datasets.
- It improves predictive accuracy for both classification and regression.
- The framework effectively captures semantic dependencies within tables.

## Abstract

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.