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边缘更新图形神经网络用于模拟表格数据中的特征交互

Pimwipa Charuthamrong¹, Colin R Simpson², Binh P Nguyen¹

¹School of Mathematics and Statistics, Victoria University of Wellington, Wellington, 6012, New Zealand.

Neural networks : the official journal of the International Neural Network Society

|February 21, 2026

概括

一个新的图形神经网络 (GNN) 在表式数据分析方面表现出色,优于XGBoost和其他GNN等传统模型. 这种深度学习方法有效地捕获功能交互,以提高机器学习性能.

科学领域:

机器学习机器学习
图形神经网络图形神经网络
数据科学数据科学数据科学

背景情况:

在机器学习中,表格式数据分析至关重要.
现有的图形神经网络 (GNN) 在应用于表格数据时面临过度平滑等挑战.
梯度增强的决策树 (例如,XGBoost,CatBoost) 是表格数据的最新技术.

研究的目的:

提出基于图形同态网络 (GIN) 的传递消息的GNN,以增强表格数据学习.
在表式数据集中建模复杂的特征相互作用.
为了解决在GNN中常见的过度平滑问题.

主要方法:

从表格数据中构建完全连接的,未加权的特征图形,使用上下文特征编码.
整合了一个分类节点,用于在推理过程中进行图形表示.
使用神经网络进行边缘属性学习,并使用剩余连接进行节点和边缘更新,以减轻过度平滑.

主要成果:

在12个数据集中的6个表式深度学习和GNN模型中获得最佳平均排名.
在所有具有默认超参数的数据集和在具有调整超参数的8个数据集上表现优于XGBoost和CatBoost.
在所有测试数据集上表现优于5个常用的或最近提出的GNN.

结论:

与现有的深度学习和GNN模型相比,拟议的GNN架构在表格数据上表现出卓越的性能.
该模型有效地处理特征交互并减轻过度平滑,为梯度增强树提供了有竞争力的替代方案.
这种GNN方法为表式数据机器学习任务提供了一个强大的新工具.

关键词:

深度学习是一种深度学习.边缘功能边缘功能边缘功能特性相互作用的作用.图表神经网络的神经网络表格式数据是表格式的数据.

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