Graph Neural Network contextual embedding for Deep Learning on tabular data.
0301 basic medicine
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Artificial Intelligence
Interaction Network
Tabular data
Graph Neural Network
Machine Learning (cs.LG)
03 medical and health sciences
Deep Learning
Artificial Intelligence (cs.AI)
Artificial Intelligence
1203.04 Inteligencia Artificial
Contextual embedding
1203 Ciencia de Los Ordenadores
DOI:
10.48550/arxiv.2303.06455
Publication Date:
2024-05-01
AUTHORS (4)
ABSTRACT
All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modelling interactions among tabular features. Its results outperform those of a recently published survey with DL benchmark based on five public datasets, also achieving competitive results when compared to boosted-tree solutions.
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