Abstract

Recent advancements in graph neural networks (GNNs) have highlighted their potential for addressing challenges in tabular data prediction by capturing complex inter-sample relationships and relaxing the traditional independent and identically distributed (i.i.d.) assumption. In this work, we present a novel GNN architecture, Feature-Oriented Graph Neural Networks (FOG), specifically designed for tabular data prediction. The FOG model transforms tabular data into feature-oriented graphs and incorporates a feature importance learner to identify distinct feature importance patterns across different samples, enabling it to effectively capture intricate sample interactions. Experimental results demonstrate that FOG achieves state-of-the-art performance on various real-world and synthetic datasets. It accurately identifies key features and delivers feature importance assessments that are highly consistent with those …

Date

June 10, 2025

Authors

Teng Yuan Tsou, Pei-Xuan Li, Fandel Lin, Hsun-Ping Hsieh

Book

Pacific-Asia Conference on Knowledge Discovery and Data Mining

Pages

29-40

Publisher

Springer Nature Singapore