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oai:uvadoc.uva.es:10324/729382025-01-29T10:08:05Zcom_10324_1151com_10324_931com_10324_894col_10324_1278

Villaizán Vallelado, Mario Salvatori, Matteo Carro Martínez, Belén Sánchez Esguevillas, Antonio Javier 2024-12-20T09:06:26Z 2024-12-20T09:06:26Z 2024 Neural Networks, mayo 2024, vol. 173, 106180 0893-6080 https://uvadoc.uva.es/handle/10324/72938 10.1016/j.neunet.2024.106180 106180 Neural Networks 173 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. eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2024 The Authors Attribution-NonCommercial-NoDerivatives 4.0 Internacional Graph Neural Network contextual embedding for Deep Learning on tabular data info:eu-repo/semantics/article