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Graph Neural Network contextual embedding for Deep Learning on tabular data

Published: 02 July 2024 Publication History

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 modeling interactions among tabular features. Its results outperform those of a recently published survey with DL benchmark based on seven public datasets, also achieving competitive results when compared to boosted-tree solutions.

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Published In

cover image Neural Networks
Neural Networks  Volume 173, Issue C
May 2024
703 pages

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Elsevier Science Ltd.

United Kingdom

Publication History

Published: 02 July 2024

Author Tags

  1. Deep Learning
  2. Graph Neural Network
  3. Interaction Network
  4. Contextual embedding
  5. Tabular data
  6. Artificial Intelligence

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